C2W2_Assignment.ipynb

{
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   "metadata": {
    "id": "23R0Z9RojXYW"
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   "source": [
    "# Week 2 Assignment: Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sfSQ-kX-MLEr"
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   "source": [
    "For this week's assignment, you will build a data pipeline using using [Tensorflow Extended (TFX)](https://www.tensorflow.org/tfx) to prepare features from the [Metro Interstate Traffic Volume dataset](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume). Try to only use the documentation and code hints to accomplish the tasks but feel free to review the 2nd ungraded lab this week in case you get stuck.\n",
    "\n",
    "Upon completion, you will have:\n",
    "\n",
    "* created an InteractiveContext to run TFX components interactively\n",
    "* used TFX ExampleGen component to split your dataset into training and evaluation datasets\n",
    "* generated the statistics and the schema of your dataset using TFX StatisticsGen and SchemaGen components\n",
    "* validated the evaluation dataset statistics using TFX ExampleValidator\n",
    "* performed feature engineering using the TFX Transform component\n",
    "\n",
    "Let's begin!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "\n",
    "- [1 - Setup](#1)\n",
    "  - [1.1 - Imports](#1-1)\n",
    "  - [1.2 - Define Paths](#1-2)\n",
    "  - [1.3 - Preview the Dataset](#1-3)\n",
    "  - [1.4 - Create the InteractiveContext](#1-4)\n",
    "- [2 - Run TFX components interactively](#2)\n",
    "  - [2.1 - ExampleGen](#2-1)\n",
    "    - [Exercise 1 - ExampleGen](#ex-1)\n",
    "    - [Exercise 2 - get_records()](#ex-2)\n",
    "  - [2.2 - StatisticsGen](#2-2)\n",
    "    - [Exercise 3 - StatisticsGen](#ex-3)\n",
    "  - [2.3 - SchemaGen](#2-3)\n",
    "    - [Exercise 4 - SchemaGen](#ex-4)\n",
    "  - [2.4 - ExampleValidator](#2-4)\n",
    "    - [Exercise 5 - ExampleValidator](#ex-5)\n",
    "  - [2.5 - Transform](#2-5)\n",
    "    - [Exercise 6 - preprocessing_fn()](#ex-6)\n",
    "    - [Exercise 7 - Transform](#ex-7)"
   ]
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "\u001b[32mGrader metadata detected! You can proceed with the lab!\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "# IMPORTANT: This will check your notebook's metadata for grading.\n",
    "# Please do not continue the lab unless the output of this cell tells you to proceed. \n",
    "!python add_metadata.py --filename C2W2_Assignment.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_**NOTE:** To prevent errors from the autograder, you are not allowed to edit or delete non-graded cells in this notebook . Please only put your solutions in between the `### START CODE HERE` and `### END CODE HERE` code comments, and also refrain from adding any new cells. **Once you have passed this assignment** and want to experiment with any of the non-graded code, you may follow the instructions at the bottom of this notebook._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2GivNBNYjb3b"
   },
   "source": [
    "<a name='1'></a>\n",
    "## 1 - Setup\n",
    "As usual, you will first need to import the necessary packages. For reference, the lab environment uses *TensorFlow version: 2.6* and *TFX version: 1.3*."
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "N-ePgV0Lj68Q"
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   "source": [
    "<a name='1-1'></a>\n",
    "### 1.1 Imports"
   ]
  },
  {
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   "execution_count": 6,
   "metadata": {
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    "editable": false,
    "id": "YIqpWK9efviJ",
    "tags": [
     "graded"
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   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "import os\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from tfx import v1 as tfx\n",
    "import tensorflow_transform.beam as tft_beam\n",
    "from google.protobuf.json_format import MessageToDict\n",
    "from tensorflow_transform.tf_metadata import dataset_metadata, schema_utils\n",
    "from tfx.orchestration.experimental.interactive.interactive_context import InteractiveContext\n",
    "\n",
    "import tempfile\n",
    "import pprint\n",
    "import warnings\n",
    "\n",
    "pp = pprint.PrettyPrinter()\n",
    "\n",
    "# ignore tf warning messages\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1-2'></a>\n",
    "### 1.2 - Define paths\n",
    "\n",
    "You will define a few global variables to indicate paths in the local workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "graded"
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   "source": [
    "# grader-required-cell\n",
    "\n",
    "# location of the pipeline metadata store\n",
    "_pipeline_root = './pipeline'\n",
    "\n",
    "# directory of the raw data files\n",
    "_data_root = './data'\n",
    "\n",
    "# path to the raw training data\n",
    "_data_filepath = os.path.join(_data_root, 'metro_traffic_volume.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n2cMMAbSkGfX"
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   "source": [
    "<a name='1-3'></a>\n",
    "### 1.3 - Preview the  dataset\n",
    "\n",
    "The [Metro Interstate Traffic Volume dataset](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume) contains hourly traffic volume of a road in Minnesota from 2012-2018. With this data, you can develop a model for predicting the traffic volume given the date, time, and weather conditions. The attributes are:\n",
    "\n",
    "* **holiday** - US National holidays plus regional holiday, Minnesota State Fair\n",
    "* **temp** - Average temp in Kelvin\n",
    "* **rain_1h** - Amount in mm of rain that occurred in the hour\n",
    "* **snow_1h** - Amount in mm of snow that occurred in the hour\n",
    "* **clouds_all** - Percentage of cloud cover\n",
    "* **weather_main** - Short textual description of the current weather\n",
    "* **weather_description** - Longer textual description of the current weather\n",
    "* **date_time** - DateTime Hour of the data collected in local CST time\n",
    "* **traffic_volume** - Numeric Hourly I-94 ATR 301 reported westbound traffic volume\n",
    "* **month** - taken from date_time\n",
    "* **day** - taken from date_time\n",
    "* **day_of_week** - taken from date_time\n",
    "* **hour** - taken from date_time\n",
    "\n",
    "\n",
    "*Disclaimer: We added the last four attributes shown above (i.e. month, day, day_of_week, hour) to the original dataset to increase the features you can transform later.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "blZC1sIQOWfH"
   },
   "source": [
    "Take a quick look at the first few rows of the CSV file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "c5YPeLPFOXaD",
    "tags": [
     "graded"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "holiday,temp,rain_1h,snow_1h,clouds_all,weather_main,weather_description,date_time,traffic_volume,month,day,day_of_week,hour\r\n",
      "None,288.28,0.0,0.0,40,Clouds,scattered clouds,2012-10-02 09:00:00,5545,10,2,1,9\r\n",
      "None,289.36,0.0,0.0,75,Clouds,broken clouds,2012-10-02 10:00:00,4516,10,2,1,10\r\n",
      "None,289.58,0.0,0.0,90,Clouds,overcast clouds,2012-10-02 11:00:00,4767,10,2,1,11\r\n",
      "None,290.13,0.0,0.0,90,Clouds,overcast clouds,2012-10-02 12:00:00,5026,10,2,1,12\r\n",
      "None,291.14,0.0,0.0,75,Clouds,broken clouds,2012-10-02 13:00:00,4918,10,2,1,13\r\n",
      "None,291.72,0.0,0.0,1,Clear,sky is clear,2012-10-02 14:00:00,5181,10,2,1,14\r\n",
      "None,293.17,0.0,0.0,1,Clear,sky is clear,2012-10-02 15:00:00,5584,10,2,1,15\r\n",
      "None,293.86,0.0,0.0,1,Clear,sky is clear,2012-10-02 16:00:00,6015,10,2,1,16\r\n",
      "None,294.14,0.0,0.0,20,Clouds,few clouds,2012-10-02 17:00:00,5791,10,2,1,17\r\n"
     ]
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Preview the dataset\n",
    "!head {_data_filepath}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8ONIE_hdkPS4"
   },
   "source": [
    "<a name='1-4'></a>\n",
    "### 1.4 - Create the InteractiveContext\n",
    "\n",
    "You will need to initialize the `InteractiveContext` to enable running the TFX components interactively. As before, you will let it create the metadata store in the `_pipeline_root` directory. You can safely ignore the warning about the missing metadata config file."
   ]
  },
  {
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   "execution_count": 9,
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    "deletable": false,
    "editable": false,
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    "tags": [
     "graded"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:InteractiveContext metadata_connection_config not provided: using SQLite ML Metadata database at ./pipeline/metadata.sqlite.\n"
     ]
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Declare the InteractiveContext and use a local sqlite file as the metadata store.\n",
    "# You can ignore the warning about the missing metadata config file\n",
    "context = InteractiveContext(pipeline_root=_pipeline_root)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HdQWxfsVkzdJ"
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   "source": [
    "<a name='2'></a>\n",
    "## 2 - Run TFX components interactively\n",
    "\n",
    "In the following exercises, you will create the data pipeline components one-by-one, run each of them, and visualize their output artifacts. Recall that we refer to the outputs of pipeline components as *artifacts* and these can be inputs to the next stage of the pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L9fwt9gQk3BR"
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   "source": [
    "<a name='2-1'></a>\n",
    "### 2.1 - ExampleGen\n",
    "\n",
    "The pipeline starts with the [ExampleGen](https://www.tensorflow.org/tfx/guide/examplegen) component. It will:\n",
    "\n",
    "*   split the data into training and evaluation sets (by default: 2/3 train, 1/3 eval).\n",
    "*   convert each data row into `tf.train.Example` format. This [protocol buffer](https://developers.google.com/protocol-buffers) is designed for Tensorflow operations and is used by the TFX components.\n",
    "*   compress and save the data collection under the `_pipeline_root` directory for other components to access. These examples are stored in `TFRecord` format. This optimizes read and write operations within Tensorflow especially if you have a large collection of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-1'></a>\n",
    "#### Exercise 1: ExampleGen\n",
    "\n",
    "Fill out the code below to ingest the data from the CSV file stored in the `_data_root` directory."
   ]
  },
  {
   "cell_type": "code",
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    "id": "PyXjuMt8f-9u",
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       "}</td></tr><tr><td class=\"attr-name\">['output_config']</td><td class = \"attrvalue\">{\n",
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       "    &quot;splits&quot;: [\n",
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       "        &quot;hash_buckets&quot;: 2,\n",
       "        &quot;name&quot;: &quot;train&quot;\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: ./pipeline/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7f183cfeef70</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: CsvExampleGen\n",
       "    execution_id: 1\n",
       "    outputs:\n",
       "        examples: Channel(\n",
       "            type_name: Examples\n",
       "            artifacts: [Artifact(artifact: id: 1\n",
       "        type_id: 14\n",
       "        uri: \"./pipeline/CsvExampleGen/examples/1\"\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value {\n",
       "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"file_format\"\n",
       "          value {\n",
       "            string_value: \"tfrecords_gzip\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"input_fingerprint\"\n",
       "          value {\n",
       "            string_value: \"split:single_split,num_files:1,total_bytes:3648458,xor_checksum:1634184281,sum_checksum:1634184281\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"payload_format\"\n",
       "          value {\n",
       "            string_value: \"FORMAT_TF_EXAMPLE\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"span\"\n",
       "          value {\n",
       "            int_value: 0\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 14\n",
       "        name: \"Examples\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        properties {\n",
       "          key: \"version\"\n",
       "          value: INT\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "### START CODE HERE\n",
    "\n",
    "# Instantiate ExampleGen with the input CSV dataset\n",
    "example_gen = tfx.components.CsvExampleGen(input_base=_data_root)\n",
    "\n",
    "# Run the component using the InteractiveContext instance\n",
    "context.run(example_gen)\n",
    "\n",
    "### END CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OqCoZh7KPUm9"
   },
   "source": [
    "You should see the output cell of the `InteractiveContext` above showing the metadata associated with the component execution. You can expand the items under `.component.outputs` and see that an `Examples` artifact for the train and eval split is created in `metro_traffic_pipeline/CsvExampleGen/examples/{execution_id}`. \n",
    "\n",
    "You can also check that programmatically with the following snippet. You can focus on the `try` block. The `except` and `else` block is needed mainly for grading. `context.run()` yields no operation when executed in a non-interactive environment (such as the grader script that runs outside of this notebook). In such scenarios, the URI must be manually set to avoid errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "880KkTAkPeUg",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "split names: [\"train\", \"eval\"]\n",
      "artifact uri: ./pipeline/CsvExampleGen/examples/1\n"
     ]
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "try:\n",
    "    # get the artifact object\n",
    "    artifact = example_gen.outputs['examples'].get()[0]\n",
    "    \n",
    "    # print split names and uri\n",
    "    print(f'split names: {artifact.split_names}')\n",
    "    print(f'artifact uri: {artifact.uri}')\n",
    "\n",
    "# for grading since context.run() does not work outside the notebook\n",
    "except IndexError:\n",
    "    print(\"context.run() was no-op\")\n",
    "    examples_path = './pipeline/CsvExampleGen/examples'\n",
    "    dir_id = os.listdir(examples_path)[0]\n",
    "    artifact_uri = f'{examples_path}/{dir_id}'\n",
    "\n",
    "else:\n",
    "    artifact_uri = artifact.uri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J6vcbW_wPqvl"
   },
   "source": [
    "The ingested data has been saved to the directory specified by the artifact Uniform Resource Identifier (URI). As a sanity check, you can take a look at some of the training examples. This requires working with Tensorflow data types, particularly `tf.train.Example` and `TFRecord` (you can read more about them [here](https://www.tensorflow.org/tutorials/load_data/tfrecord)). Let's first load the `TFRecord` into a variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "H4XIXjiCPwzQ",
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Get the URI of the output artifact representing the training examples, which is a directory\n",
    "train_uri = os.path.join(artifact_uri, 'Split-train')\n",
    "\n",
    "# Get the list of files in this directory (all compressed TFRecord files)\n",
    "tfrecord_filenames = [os.path.join(train_uri, name)\n",
    "                      for name in os.listdir(train_uri)]\n",
    "\n",
    "# Create a `TFRecordDataset` to read these files\n",
    "dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-2'></a>\n",
    "#### Exercise 2: get_records()\n",
    "\n",
    "Complete the helper function below to return a specified number of examples.\n",
    "\n",
    "*Hints: You may find the [MessageToDict](https://googleapis.dev/python/protobuf/latest/google/protobuf/json_format.html#google.protobuf.json_format.MessageToDict) helper function and tf.train.Example's [ParseFromString()](https://googleapis.dev/python/protobuf/latest/google/protobuf/message.html#google.protobuf.message.Message.ParseFromString) method useful here. You can also refer [here](https://www.tensorflow.org/tutorials/load_data/tfrecord) for a refresher on TFRecord and tf.train.Example()*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "deletable": false,
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "def get_records(dataset, num_records):\n",
    "    '''Extracts records from the given dataset.\n",
    "    Args:\n",
    "        dataset (TFRecordDataset): dataset saved by ExampleGen\n",
    "        num_records (int): number of records to preview\n",
    "    '''\n",
    "    \n",
    "    # initialize an empty list\n",
    "    records = []\n",
    "\n",
    "    ### START CODE HERE\n",
    "    # Use the `take()` method to specify how many records to get\n",
    "    for tfrecord in dataset.take(num_records):\n",
    "        \n",
    "        # Get the numpy property of the tensor\n",
    "        serialized_example = tfrecord.numpy()\n",
    "        \n",
    "        # Initialize a `tf.train.Example()` to read the serialized data\n",
    "        example = tf.train.Example()\n",
    "        \n",
    "        # Read the example data (output is a protocol buffer message)\n",
    "        example.ParseFromString(serialized_example)\n",
    "        \n",
    "        # convert the protocol bufffer message to a Python dictionary\n",
    "        example_dict = (MessageToDict(example))\n",
    "        \n",
    "        # append to the records list\n",
    "        records.append(example_dict)\n",
    "        \n",
    "    ### END CODE HERE\n",
    "    return records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'features': {'feature': {'clouds_all': {'int64List': {'value': ['40']}},\n",
      "                           'date_time': {'bytesList': {'value': ['MjAxMi0xMC0wMiAwOTowMDowMA==']}},\n",
      "                           'day': {'int64List': {'value': ['2']}},\n",
      "                           'day_of_week': {'int64List': {'value': ['1']}},\n",
      "                           'holiday': {'bytesList': {'value': ['Tm9uZQ==']}},\n",
      "                           'hour': {'int64List': {'value': ['9']}},\n",
      "                           'month': {'int64List': {'value': ['10']}},\n",
      "                           'rain_1h': {'floatList': {'value': [0.0]}},\n",
      "                           'snow_1h': {'floatList': {'value': [0.0]}},\n",
      "                           'temp': {'floatList': {'value': [288.28]}},\n",
      "                           'traffic_volume': {'int64List': {'value': ['5545']}},\n",
      "                           'weather_description': {'bytesList': {'value': ['c2NhdHRlcmVkIGNsb3Vkcw==']}},\n",
      "                           'weather_main': {'bytesList': {'value': ['Q2xvdWRz']}}}}},\n",
      " {'features': {'feature': {'clouds_all': {'int64List': {'value': ['75']}},\n",
      "                           'date_time': {'bytesList': {'value': ['MjAxMi0xMC0wMiAxMDowMDowMA==']}},\n",
      "                           'day': {'int64List': {'value': ['2']}},\n",
      "                           'day_of_week': {'int64List': {'value': ['1']}},\n",
      "                           'holiday': {'bytesList': {'value': ['Tm9uZQ==']}},\n",
      "                           'hour': {'int64List': {'value': ['10']}},\n",
      "                           'month': {'int64List': {'value': ['10']}},\n",
      "                           'rain_1h': {'floatList': {'value': [0.0]}},\n",
      "                           'snow_1h': {'floatList': {'value': [0.0]}},\n",
      "                           'temp': {'floatList': {'value': [289.36]}},\n",
      "                           'traffic_volume': {'int64List': {'value': ['4516']}},\n",
      "                           'weather_description': {'bytesList': {'value': ['YnJva2VuIGNsb3Vkcw==']}},\n",
      "                           'weather_main': {'bytesList': {'value': ['Q2xvdWRz']}}}}},\n",
      " {'features': {'feature': {'clouds_all': {'int64List': {'value': ['90']}},\n",
      "                           'date_time': {'bytesList': {'value': ['MjAxMi0xMC0wMiAxMTowMDowMA==']}},\n",
      "                           'day': {'int64List': {'value': ['2']}},\n",
      "                           'day_of_week': {'int64List': {'value': ['1']}},\n",
      "                           'holiday': {'bytesList': {'value': ['Tm9uZQ==']}},\n",
      "                           'hour': {'int64List': {'value': ['11']}},\n",
      "                           'month': {'int64List': {'value': ['10']}},\n",
      "                           'rain_1h': {'floatList': {'value': [0.0]}},\n",
      "                           'snow_1h': {'floatList': {'value': [0.0]}},\n",
      "                           'temp': {'floatList': {'value': [289.58]}},\n",
      "                           'traffic_volume': {'int64List': {'value': ['4767']}},\n",
      "                           'weather_description': {'bytesList': {'value': ['b3ZlcmNhc3QgY2xvdWRz']}},\n",
      "                           'weather_main': {'bytesList': {'value': ['Q2xvdWRz']}}}}}]\n"
     ]
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Get 3 records from the dataset\n",
    "sample_records = get_records(dataset, 3)\n",
    "\n",
    "# Print the output\n",
    "pp.pprint(sample_records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2gluYjccf-IP"
   },
   "source": [
    "You should see three of the examples printed above. Now that `ExampleGen` has finished ingesting the data, the next step is data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "csM6BFhtk5Aa"
   },
   "source": [
    "<a name='2-2'></a>\n",
    "### 2.2 - StatisticsGen\n",
    "The [StatisticsGen](https://www.tensorflow.org/tfx/guide/statsgen) component computes statistics over your dataset for data analysis, as well as for use in downstream components. It uses the [TensorFlow Data Validation](https://www.tensorflow.org/tfx/data_validation/get_started) library.\n",
    "\n",
    "`StatisticsGen` takes as input the dataset ingested using `CsvExampleGen`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-3'></a>\n",
    "#### Exercise 3: StatisticsGen\n",
    "\n",
    "Fill the code below to generate statistics from the output examples of `CsvExampleGen`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "deletable": false,
    "id": "MAscCCYWgA-9",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.8 interpreter.\n"
     ]
    },
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       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Examples'</span> (uri: ./pipeline/CsvExampleGen/examples/1)<span class=\"deemphasize\"> at 0x7f183cfeef70</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Examples&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/CsvExampleGen/examples/1</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr><tr><td class=\"attr-name\">.version</td><td class = \"attrvalue\">0</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f17fac444c0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleStatistics</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: ./pipeline/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f1839f62730</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/StatisticsGen/statistics/2</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: StatisticsGen\n",
       "    execution_id: 2\n",
       "    outputs:\n",
       "        statistics: Channel(\n",
       "            type_name: ExampleStatistics\n",
       "            artifacts: [Artifact(artifact: id: 2\n",
       "        type_id: 16\n",
       "        uri: \"./pipeline/StatisticsGen/statistics/2\"\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value {\n",
       "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"statistics\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"StatisticsGen\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 16\n",
       "        name: \"ExampleStatistics\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "### START CODE HERE\n",
    "# Instantiate StatisticsGen with the ExampleGen ingested dataset\n",
    "statistics_gen = tfx.components.StatisticsGen(\n",
    "    examples=example_gen.outputs['examples'])\n",
    "    \n",
    "\n",
    "# Run the component\n",
    "context.run(statistics_gen)\n",
    "### END CODE HERE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Artifact at ./pipeline/StatisticsGen/statistics/2</b><br/><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><b>'train' split:</b></div><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "        facets_iframe.srcdoc = facets_html;\n",
       "         facets_iframe.id = \"\";\n",
       "         setTimeout(() => {\n",
       "           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
       "         }, 1500)\n",
       "         </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><b>'eval' split:</b></div><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "        facets_iframe.srcdoc = facets_html;\n",
       "         facets_iframe.id = \"\";\n",
       "         setTimeout(() => {\n",
       "           facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
       "         }, 1500)\n",
       "         </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Plot the statistics generated\n",
    "context.show(statistics_gen.outputs['statistics'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HLKLTO9Nk60p"
   },
   "source": [
    "<a name='2-3'></a>\n",
    "### 2.3 - SchemaGen\n",
    "\n",
    "The [SchemaGen](https://www.tensorflow.org/tfx/guide/schemagen) component also uses TFDV to generate a schema based on your data statistics. As you've learned previously, a schema defines the expected bounds, types, and properties of the features in your dataset.\n",
    "\n",
    "`SchemaGen` will take as input the statistics that we generated with `StatisticsGen`, looking at the training split by default."
   ]
  },
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   "source": [
    "<a name='ex-4'></a>\n",
    "#### Exercise 4: SchemaGen"
   ]
  },
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: ./pipeline/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f1839f62730</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/StatisticsGen/statistics/2</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['schema']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f17faba2d00</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Schema</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
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       "}\n",
       ".tfx-object, .tfx-object * {\n",
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       "  color: #999999;\n",
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       "  content: '▼';\n",
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       "  opacity: 0.5;\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: ./pipeline/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f17faa173a0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Schema&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/SchemaGen/schema/3</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: SchemaGen\n",
       "    execution_id: 3\n",
       "    outputs:\n",
       "        schema: Channel(\n",
       "            type_name: Schema\n",
       "            artifacts: [Artifact(artifact: id: 3\n",
       "        type_id: 18\n",
       "        uri: \"./pipeline/SchemaGen/schema/3\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"schema\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"SchemaGen\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 18\n",
       "        name: \"Schema\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "### START CODE HERE\n",
    "# Instantiate SchemaGen with the output statistics from the StatisticsGen\n",
    "schema_gen = tfx.components.SchemaGen(\n",
    "    statistics=statistics_gen.outputs['statistics'],\n",
    "    )\n",
    "    \n",
    "    \n",
    "\n",
    "# Run the component\n",
    "context.run(schema_gen)\n",
    "### END CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zi6TxTUKXM6b"
   },
   "source": [
    "If all went well, you can now visualize the generated schema as a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "Ec9vqDXpXeMb",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Artifact at ./pipeline/SchemaGen/schema/3</b><br/><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>Type</th>\n",
       "      <th>Presence</th>\n",
       "      <th>Valency</th>\n",
       "      <th>Domain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature name</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'date_time'</th>\n",
       "      <td>BYTES</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'holiday'</th>\n",
       "      <td>STRING</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>'holiday'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'weather_description'</th>\n",
       "      <td>STRING</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>'weather_description'</td>\n",
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       "    <tr>\n",
       "      <th>'weather_main'</th>\n",
       "      <td>STRING</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>'weather_main'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'clouds_all'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'day'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'day_of_week'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'hour'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'month'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'rain_1h'</th>\n",
       "      <td>FLOAT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'snow_1h'</th>\n",
       "      <td>FLOAT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'temp'</th>\n",
       "      <td>FLOAT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'traffic_volume'</th>\n",
       "      <td>INT</td>\n",
       "      <td>required</td>\n",
       "      <td></td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Type  Presence Valency                 Domain\n",
       "Feature name                                                          \n",
       "'date_time'             BYTES  required                              -\n",
       "'holiday'              STRING  required                      'holiday'\n",
       "'weather_description'  STRING  required          'weather_description'\n",
       "'weather_main'         STRING  required                 'weather_main'\n",
       "'clouds_all'              INT  required                              -\n",
       "'day'                     INT  required                              -\n",
       "'day_of_week'             INT  required                              -\n",
       "'hour'                    INT  required                              -\n",
       "'month'                   INT  required                              -\n",
       "'rain_1h'               FLOAT  required                              -\n",
       "'snow_1h'               FLOAT  required                              -\n",
       "'temp'                  FLOAT  required                              -\n",
       "'traffic_volume'          INT  required                              -"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Domain</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'holiday'</th>\n",
       "      <td>'Christmas Day', 'Columbus Day', 'Independence Day', 'Labor Day', 'Martin Luther King Jr Day', 'Memorial Day', 'New Years Day', 'None', 'State Fair', 'Thanksgiving Day', 'Veterans Day', 'Washingtons Birthday'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'weather_description'</th>\n",
       "      <td>'SQUALLS', 'Sky is Clear', 'broken clouds', 'drizzle', 'few clouds', 'fog', 'freezing rain', 'haze', 'heavy intensity drizzle', 'heavy intensity rain', 'heavy snow', 'light intensity drizzle', 'light intensity shower rain', 'light rain', 'light rain and snow', 'light shower snow', 'light snow', 'mist', 'moderate rain', 'overcast clouds', 'proximity shower rain', 'proximity thunderstorm', 'proximity thunderstorm with drizzle', 'proximity thunderstorm with rain', 'scattered clouds', 'shower drizzle', 'sky is clear', 'sleet', 'smoke', 'snow', 'thunderstorm', 'thunderstorm with heavy rain', 'thunderstorm with light drizzle', 'thunderstorm with light rain', 'thunderstorm with rain', 'very heavy rain', 'shower snow', 'thunderstorm with drizzle'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'weather_main'</th>\n",
       "      <td>'Clear', 'Clouds', 'Drizzle', 'Fog', 'Haze', 'Mist', 'Rain', 'Smoke', 'Snow', 'Squall', 'Thunderstorm'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Values\n",
       "Domain                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             \n",
       "'holiday'              'Christmas Day', 'Columbus Day', 'Independence Day', 'Labor Day', 'Martin Luther King Jr Day', 'Memorial Day', 'New Years Day', 'None', 'State Fair', 'Thanksgiving Day', 'Veterans Day', 'Washingtons Birthday'                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            \n",
       "'weather_description'  'SQUALLS', 'Sky is Clear', 'broken clouds', 'drizzle', 'few clouds', 'fog', 'freezing rain', 'haze', 'heavy intensity drizzle', 'heavy intensity rain', 'heavy snow', 'light intensity drizzle', 'light intensity shower rain', 'light rain', 'light rain and snow', 'light shower snow', 'light snow', 'mist', 'moderate rain', 'overcast clouds', 'proximity shower rain', 'proximity thunderstorm', 'proximity thunderstorm with drizzle', 'proximity thunderstorm with rain', 'scattered clouds', 'shower drizzle', 'sky is clear', 'sleet', 'smoke', 'snow', 'thunderstorm', 'thunderstorm with heavy rain', 'thunderstorm with light drizzle', 'thunderstorm with light rain', 'thunderstorm with rain', 'very heavy rain', 'shower snow', 'thunderstorm with drizzle'\n",
       "'weather_main'         'Clear', 'Clouds', 'Drizzle', 'Fog', 'Haze', 'Mist', 'Rain', 'Smoke', 'Snow', 'Squall', 'Thunderstorm'                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Visualize the output\n",
    "context.show(schema_gen.outputs['schema'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kZWWdbA-m7zp"
   },
   "source": [
    "Each attribute in your dataset shows up as a row in the schema table, alongside its properties. The schema also captures all the values that a categorical feature takes on, denoted as its domain.\n",
    "\n",
    "This schema will be used to detect anomalies in the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V1qcUuO9k9f8"
   },
   "source": [
    "<a name='2-4'></a>\n",
    "### 2.4 - ExampleValidator\n",
    "\n",
    "The [ExampleValidator](https://www.tensorflow.org/tfx/guide/exampleval) component detects anomalies in your data based on the generated schema from the previous step. Like the previous two components, it also uses TFDV under the hood. \n",
    "\n",
    "`ExampleValidator` will take as input the statistics from `StatisticsGen` and the schema from `SchemaGen`. By default, it compares the statistics from the evaluation split to the schema from the training split."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2-4'></a>\n",
    "#### Exercise 5: ExampleValidator\n",
    "\n",
    "Fill the code below to detect anomalies in your datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "deletable": false,
    "id": "XRlRUuGgiXks",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<script>\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">ExampleValidator</span><span class=\"deemphasize\"> at 0x7f18037492b0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f17fac444c0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleStatistics</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object .expansion-marker {\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object .class-name {\n",
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       ".tfx-object .deemphasize {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: ./pipeline/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f1839f62730</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/StatisticsGen/statistics/2</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['schema']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
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       ".tfx-object, .tfx-object * {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
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       "</style>\n",
       "<script>\n",
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       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f17faba2d00</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Schema</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object .expansion-marker {\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object table.attr-table {\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
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       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: ./pipeline/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f17faa173a0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Schema&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/SchemaGen/schema/3</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['anomalies']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
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       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
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       ".tfx-object, .tfx-object * {\n",
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       ".tfx-object > .title {\n",
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       ".tfx-object .expansion-marker {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
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       ".tfx-object table.attr-table {\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f1803749130</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleAnomalies</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
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       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
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       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (uri: ./pipeline/ExampleValidator/anomalies/4)<span class=\"deemphasize\"> at 0x7f183970bbe0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleAnomalies&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/ExampleValidator/anomalies/4</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.exec_properties</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['exclude_splits']</td><td class = \"attrvalue\">[]</td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">.component.inputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['statistics']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
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       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       "  font-weight: bold;\n",
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       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
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       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f17fac444c0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleStatistics</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
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       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object .expansion-marker {\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
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       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
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       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleStatistics'</span> (uri: ./pipeline/StatisticsGen/statistics/2)<span class=\"deemphasize\"> at 0x7f1839f62730</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleStatistics&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/StatisticsGen/statistics/2</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr><tr><td class=\"attr-name\">['schema']</td><td class = \"attrvalue\"><style>\n",
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       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'Schema'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f17faba2d00</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">Schema</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
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       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
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       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
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       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
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       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
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       ".tfx-object table.attr-table td.attr-name {\n",
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       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
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       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'Schema'</span> (uri: ./pipeline/SchemaGen/schema/3)<span class=\"deemphasize\"> at 0x7f17faa173a0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.Schema&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/SchemaGen/schema/3</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr><tr><td class=\"attr-name\">.component.outputs</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">['anomalies']</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Channel</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (1 artifact)<span class=\"deemphasize\"> at 0x7f1803749130</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type_name</td><td class = \"attrvalue\">ExampleAnomalies</td></tr><tr><td class=\"attr-name\">._artifacts</td><td class = \"attrvalue\"><table class=\"attr-table\"><tr><td class=\"attr-name\">[0]</td><td class = \"attrvalue\"><style>\n",
       ".tfx-object.expanded {\n",
       "  padding: 4px 8px 4px 8px;\n",
       "  background: white;\n",
       "  border: 1px solid #bbbbbb;\n",
       "  box-shadow: 4px 4px 2px rgba(0,0,0,0.05);\n",
       "}\n",
       ".tfx-object, .tfx-object * {\n",
       "  font-size: 11pt;\n",
       "}\n",
       ".tfx-object > .title {\n",
       "  cursor: pointer;\n",
       "}\n",
       ".tfx-object .expansion-marker {\n",
       "  color: #999999;\n",
       "}\n",
       ".tfx-object.expanded > .title > .expansion-marker:before {\n",
       "  content: '▼';\n",
       "}\n",
       ".tfx-object.collapsed > .title > .expansion-marker:before {\n",
       "  content: '▶';\n",
       "}\n",
       ".tfx-object .class-name {\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object .deemphasize {\n",
       "  opacity: 0.5;\n",
       "}\n",
       ".tfx-object.collapsed > table.attr-table {\n",
       "  display: none;\n",
       "}\n",
       ".tfx-object.expanded > table.attr-table {\n",
       "  display: block;\n",
       "}\n",
       ".tfx-object table.attr-table {\n",
       "  border: 2px solid white;\n",
       "  margin-top: 5px;\n",
       "}\n",
       ".tfx-object table.attr-table td.attr-name {\n",
       "  vertical-align: top;\n",
       "  font-weight: bold;\n",
       "}\n",
       ".tfx-object table.attr-table td.attrvalue {\n",
       "  text-align: left;\n",
       "}\n",
       "</style>\n",
       "<script>\n",
       "function toggleTfxObject(element) {\n",
       "  var objElement = element.parentElement;\n",
       "  if (objElement.classList.contains('collapsed')) {\n",
       "    objElement.classList.remove('collapsed');\n",
       "    objElement.classList.add('expanded');\n",
       "  } else {\n",
       "    objElement.classList.add('collapsed');\n",
       "    objElement.classList.remove('expanded');\n",
       "  }\n",
       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (uri: ./pipeline/ExampleValidator/anomalies/4)<span class=\"deemphasize\"> at 0x7f183970bbe0</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleAnomalies&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/ExampleValidator/anomalies/4</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\">[&quot;train&quot;, &quot;eval&quot;]</td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: ExampleValidator\n",
       "    execution_id: 4\n",
       "    outputs:\n",
       "        anomalies: Channel(\n",
       "            type_name: ExampleAnomalies\n",
       "            artifacts: [Artifact(artifact: id: 4\n",
       "        type_id: 20\n",
       "        uri: \"./pipeline/ExampleValidator/anomalies/4\"\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value {\n",
       "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"anomalies\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"ExampleValidator\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 20\n",
       "        name: \"ExampleAnomalies\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "### START CODE HERE\n",
    "# Instantiate ExampleValidator with the statistics and schema from the previous steps\n",
    "example_validator = tfx.components.ExampleValidator(\n",
    "    statistics=statistics_gen.outputs['statistics'],\n",
    "    schema=schema_gen.outputs['schema'])\n",
    "    \n",
    "    \n",
    "\n",
    "# Run the component\n",
    "context.run(example_validator)\n",
    "### END CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "855mrHgJcoer"
   },
   "source": [
    "As with the previous steps, you can visualize the anomalies as a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "TDyAAozQcrk3",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Artifact at ./pipeline/ExampleValidator/anomalies/4</b><br/><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><b>'train' split:</b></div><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4 style=\"color:green;\">No anomalies found.</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div><b>'eval' split:</b></div><br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h4 style=\"color:green;\">No anomalies found.</h4>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Visualize the output\n",
    "context.show(example_validator.outputs['anomalies'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "znMoJj60ybZx"
   },
   "source": [
    "If there are anomalies detected, you should examine how you should handle it. For example, you can relax distribution constraints or modify the domain of some features. You've had some practice with this last week when you used TFDV and you can also do that here. \n",
    "\n",
    "For this particular case, there should be no anomalies detected and we can proceed to the next step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JPViEz5RlA36"
   },
   "source": [
    "<a name='2-5'></a>\n",
    "### 2.5 - Transform\n",
    "\n",
    "In this section, you will use the [Transform](https://www.tensorflow.org/tfx/guide/transform) component to perform feature engineering.\n",
    "\n",
    "`Transform` will take as input the data from `ExampleGen`, the schema from `SchemaGen`, as well as a module containing the preprocessing function.\n",
    "\n",
    "The component expects an external module for your Transform code so you need to use the magic command `%% writefile` to save the file to disk. We have defined a few constants that group the data's attributes according to the transforms you will perform later. This file will also be saved locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "PuNSiUKb4YJf",
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Set the constants module filename\n",
    "_traffic_constants_module_file = 'traffic_constants.py'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "HPjhXuIF4YJh",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing traffic_constants.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile {_traffic_constants_module_file}\n",
    "\n",
    "# Features to be scaled to the z-score\n",
    "DENSE_FLOAT_FEATURE_KEYS = ['temp', 'snow_1h']\n",
    "\n",
    "# Features to bucketize\n",
    "BUCKET_FEATURE_KEYS = ['rain_1h']\n",
    "\n",
    "# Number of buckets used by tf.transform for encoding each feature.\n",
    "FEATURE_BUCKET_COUNT = {'rain_1h': 3}\n",
    "\n",
    "# Feature to scale from 0 to 1\n",
    "RANGE_FEATURE_KEYS = ['clouds_all']\n",
    "\n",
    "# Number of vocabulary terms used for encoding VOCAB_FEATURES by tf.transform\n",
    "VOCAB_SIZE = 1000\n",
    "\n",
    "# Count of out-of-vocab buckets in which unrecognized VOCAB_FEATURES are hashed.\n",
    "OOV_SIZE = 10\n",
    "\n",
    "# Features with string data types that will be converted to indices\n",
    "VOCAB_FEATURE_KEYS = [\n",
    "    'holiday',\n",
    "    'weather_main',\n",
    "    'weather_description'\n",
    "]\n",
    "\n",
    "# Features with int data type that will be kept as is\n",
    "CATEGORICAL_FEATURE_KEYS = [\n",
    "    'hour', 'day', 'day_of_week', 'month'\n",
    "]\n",
    "\n",
    "# Feature to predict\n",
    "VOLUME_KEY = 'traffic_volume'\n",
    "\n",
    "def transformed_name(key):\n",
    "    return key + '_xf'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-6'></a>\n",
    "#### Exercise 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Duj2Ax5z4YJl"
   },
   "source": [
    "Next, you will fill out the transform module. As mentioned, this will also be saved to disk. Specifically, you will complete the `preprocessing_fn` which defines the transformations. See the code comments for instructions and refer to the [tft module documentation](https://www.tensorflow.org/tfx/transform/api_docs/python/tft) to look up which function to use for a given group of keys.\n",
    "\n",
    "For the label (i.e. `VOLUME_KEY`), you will transform it to indicate if it is greater than the mean of the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "4AJ9hBs94YJm",
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Set the transform module filename\n",
    "_traffic_transform_module_file = 'traffic_transform.py'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "deletable": false,
    "graded": true,
    "id": "MYmxxx9A4YJn",
    "name": "preprocessing_fn_code",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting traffic_transform.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile {_traffic_transform_module_file}\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_transform as tft\n",
    "\n",
    "import traffic_constants\n",
    "\n",
    "# Unpack the contents of the constants module\n",
    "_DENSE_FLOAT_FEATURE_KEYS = traffic_constants.DENSE_FLOAT_FEATURE_KEYS\n",
    "_RANGE_FEATURE_KEYS = traffic_constants.RANGE_FEATURE_KEYS\n",
    "_VOCAB_FEATURE_KEYS = traffic_constants.VOCAB_FEATURE_KEYS\n",
    "_VOCAB_SIZE = traffic_constants.VOCAB_SIZE\n",
    "_OOV_SIZE = traffic_constants.OOV_SIZE\n",
    "_CATEGORICAL_FEATURE_KEYS = traffic_constants.CATEGORICAL_FEATURE_KEYS\n",
    "_BUCKET_FEATURE_KEYS = traffic_constants.BUCKET_FEATURE_KEYS\n",
    "_FEATURE_BUCKET_COUNT = traffic_constants.FEATURE_BUCKET_COUNT\n",
    "_VOLUME_KEY = traffic_constants.VOLUME_KEY\n",
    "_transformed_name = traffic_constants.transformed_name\n",
    "\n",
    "\n",
    "def preprocessing_fn(inputs):\n",
    "    \"\"\"tf.transform's callback function for preprocessing inputs.\n",
    "    Args:\n",
    "    inputs: map from feature keys to raw not-yet-transformed features.\n",
    "    Returns:\n",
    "    Map from string feature key to transformed feature operations.\n",
    "    \"\"\"\n",
    "    outputs = {}\n",
    "\n",
    "    ### START CODE HERE\n",
    "    \n",
    "    # Scale these features to the z-score.\n",
    "    for key in _DENSE_FLOAT_FEATURE_KEYS:\n",
    "        # Scale these features to the z-score.\n",
    "        outputs[_transformed_name(key)] = tft.scale_to_z_score(inputs[key])\n",
    "            \n",
    "\n",
    "    # Scale these feature/s from 0 to 1\n",
    "    for key in _RANGE_FEATURE_KEYS:\n",
    "        outputs[_transformed_name(key)] = tft.scale_to_0_1(inputs[key])\n",
    "            \n",
    "\n",
    "    # Transform the strings into indices \n",
    "    # hint: use the VOCAB_SIZE and OOV_SIZE to define the top_k and num_oov parameters\n",
    "    for key in _VOCAB_FEATURE_KEYS:\n",
    "        outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(\n",
    "            inputs[key], \n",
    "            top_k=_VOCAB_SIZE, \n",
    "            num_oov_buckets=_OOV_SIZE)\n",
    "            \n",
    "            \n",
    "            \n",
    "\n",
    "    # Bucketize the feature\n",
    "    for key in _BUCKET_FEATURE_KEYS:\n",
    "        outputs[_transformed_name(key)] = tft.bucketize(\n",
    "            inputs[key], \n",
    "            _FEATURE_BUCKET_COUNT[key])\n",
    "            \n",
    "\n",
    "    # Keep the features as is. No tft function needed.\n",
    "    for key in _CATEGORICAL_FEATURE_KEYS:\n",
    "        outputs[_transformed_name(key)] = inputs[key]\n",
    "\n",
    "        \n",
    "    # Use `tf.cast` to cast the label key to float32\n",
    "    traffic_volume = tf.cast(inputs[_VOLUME_KEY], tf.float32)\n",
    "  \n",
    "    \n",
    "    # Create a feature that shows if the traffic volume is greater than the mean and cast to an int\n",
    "    outputs[_transformed_name(_VOLUME_KEY)] = tf.cast(          \n",
    "        # Use `tf.greater` to check if the traffic volume in a row is greater than the mean of the entire traffic volumn column\n",
    "        # Hint: Use a `tft` function to compute the mean.\n",
    "        tf.greater(traffic_volume, tft.mean(tf.cast(inputs[_VOLUME_KEY], tf.float32))),\n",
    "        \n",
    "        tf.int64)                                        \n",
    "    ### END CODE HERE\n",
    "    return outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:apache_beam.options.pipeline_options:Discarding unparseable args: ['/opt/conda/lib/python3.8/site-packages/ipykernel_launcher.py', '-f', '/home/jovyan/.local/share/jupyter/runtime/kernel-201e384b-889b-4cda-9870-4d388047ae20.json']\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.8 interpreter.\n"
     ]
    }
   ],
   "source": [
    "# Test your preprocessing_fn\n",
    "\n",
    "import traffic_transform\n",
    "from testing_values import feature_description, raw_data\n",
    "\n",
    "# NOTE: These next two lines are for reloading your traffic_transform module in case you need to \n",
    "# update your initial solution and re-run this cell. Please do not remove them especially if you\n",
    "# have revised your solution. Else, your changes will not be detected.\n",
    "import importlib\n",
    "importlib.reload(traffic_transform)\n",
    "\n",
    "raw_data_metadata = dataset_metadata.DatasetMetadata(schema_utils.schema_from_feature_spec(feature_description))\n",
    "\n",
    "with tft_beam.Context(temp_dir=tempfile.mkdtemp()):\n",
    "    transformed_dataset, _ = (\n",
    "        (raw_data, raw_data_metadata) | tft_beam.AnalyzeAndTransformDataset(traffic_transform.preprocessing_fn))\n",
    "\n",
    "transformed_data, transformed_metadata = transformed_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'clouds_all_xf': 1.0,\n",
       "  'day_of_week_xf': 4,\n",
       "  'day_xf': 8,\n",
       "  'holiday_xf': 0,\n",
       "  'hour_xf': 15,\n",
       "  'month_xf': 1,\n",
       "  'rain_1h_xf': 2,\n",
       "  'snow_1h_xf': 0.0,\n",
       "  'temp_xf': 0.0,\n",
       "  'traffic_volume_xf': 0,\n",
       "  'weather_description_xf': 0,\n",
       "  'weather_main_xf': 0}]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test that the transformed data matches the expected output\n",
    "transformed_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output:**\n",
    "\n",
    "```\n",
    "[{'clouds_all_xf': 1.0,\n",
    "  'day_of_week_xf': 4,\n",
    "  'day_xf': 8,\n",
    "  'holiday_xf': 0,\n",
    "  'hour_xf': 15,\n",
    "  'month_xf': 1,\n",
    "  'rain_1h_xf': 2,\n",
    "  'snow_1h_xf': 0.0,\n",
    "  'temp_xf': 0.0,\n",
    "  'traffic_volume_xf': 0,\n",
    "  'weather_description_xf': 0,\n",
    "  'weather_main_xf': 0}]\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'feature': [{'name': 'clouds_all_xf',\n",
       "   'type': 'FLOAT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'day_of_week_xf',\n",
       "   'type': 'INT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'day_xf',\n",
       "   'type': 'INT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'holiday_xf',\n",
       "   'type': 'INT',\n",
       "   'intDomain': {'isCategorical': True},\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'hour_xf',\n",
       "   'type': 'INT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'month_xf',\n",
       "   'type': 'INT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'rain_1h_xf',\n",
       "   'type': 'INT',\n",
       "   'intDomain': {'isCategorical': True},\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'snow_1h_xf',\n",
       "   'type': 'FLOAT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'temp_xf',\n",
       "   'type': 'FLOAT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'traffic_volume_xf',\n",
       "   'type': 'INT',\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'weather_description_xf',\n",
       "   'type': 'INT',\n",
       "   'intDomain': {'isCategorical': True},\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}},\n",
       "  {'name': 'weather_main_xf',\n",
       "   'type': 'INT',\n",
       "   'intDomain': {'isCategorical': True},\n",
       "   'presence': {'minFraction': 1.0},\n",
       "   'shape': {}}]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test that the transformed metadata's schema matches the expected output\n",
    "MessageToDict(transformed_metadata.schema)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output:**\n",
    "\n",
    "```\n",
    "{'feature': [{'name': 'clouds_all_xf',\n",
    "   'type': 'FLOAT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'day_of_week_xf',\n",
    "   'type': 'INT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'day_xf',\n",
    "   'type': 'INT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'holiday_xf',\n",
    "   'type': 'INT',\n",
    "   'intDomain': {'isCategorical': True},\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'hour_xf',\n",
    "   'type': 'INT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'month_xf',\n",
    "   'type': 'INT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'rain_1h_xf',\n",
    "   'type': 'INT',\n",
    "   'intDomain': {'isCategorical': True},\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'snow_1h_xf',\n",
    "   'type': 'FLOAT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'temp_xf',\n",
    "   'type': 'FLOAT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'traffic_volume_xf',\n",
    "   'type': 'INT',\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'weather_description_xf',\n",
    "   'type': 'INT',\n",
    "   'intDomain': {'isCategorical': True},\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}},\n",
    "  {'name': 'weather_main_xf',\n",
    "   'type': 'INT',\n",
    "   'intDomain': {'isCategorical': True},\n",
    "   'presence': {'minFraction': 1.0},\n",
    "   'shape': {}}]}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-7'></a>\n",
    "#### Exercise 7\n",
    "\n",
    "With the transform module defined, complete the code below to perform feature engineering on the raw data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "deletable": false,
    "id": "jHfhth_GiZI9",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.\n",
      "WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n",
      "WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n",
      "WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_2/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n",
      "WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n",
      "WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_1/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n",
      "WARNING:absl:Tables initialized inside a tf.function  will be re-initialized on every invocation of the function. This  re-initialization can have significant impact on performance. Consider lifting  them out of the graph context using  `tf.init_scope`.: compute_and_apply_vocabulary_2/apply_vocab/text_file_init/InitializeTableFromTextFileV2\n",
      "WARNING:root:This output type hint will be ignored and not used for type-checking purposes. Typically, output type hints for a PTransform are single (or nested) types wrapped by a PCollection, PDone, or None. Got: Tuple[Dict[str, Union[NoneType, _Dataset]], Union[Dict[str, Dict[str, PCollection]], NoneType], int] instead.\n",
      "WARNING:root:Make sure that locally built Python SDK docker image has Python 3.8 interpreter.\n"
     ]
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       "}\n",
       "</script>\n",
       "<div class=\"tfx-object collapsed\"><div class = \"title\" onclick=\"toggleTfxObject(this)\"><span class=\"expansion-marker\"></span><span class=\"class-name\">Artifact</span> of type <span class=\"class-name\">'ExampleAnomalies'</span> (uri: ./pipeline/Transform/post_transform_anomalies/5)<span class=\"deemphasize\"> at 0x7f17fafc8370</span></div><table class=\"attr-table\"><tr><td class=\"attr-name\">.type</td><td class = \"attrvalue\">&lt;class &#x27;tfx.types.standard_artifacts.ExampleAnomalies&#x27;&gt;</td></tr><tr><td class=\"attr-name\">.uri</td><td class = \"attrvalue\">./pipeline/Transform/post_transform_anomalies/5</td></tr><tr><td class=\"attr-name\">.span</td><td class = \"attrvalue\">0</td></tr><tr><td class=\"attr-name\">.split_names</td><td class = \"attrvalue\"></td></tr></table></div></td></tr></table></td></tr></table></div></td></tr></table></td></tr></table></div>"
      ],
      "text/plain": [
       "ExecutionResult(\n",
       "    component_id: Transform\n",
       "    execution_id: 5\n",
       "    outputs:\n",
       "        transform_graph: Channel(\n",
       "            type_name: TransformGraph\n",
       "            artifacts: [Artifact(artifact: id: 5\n",
       "        type_id: 22\n",
       "        uri: \"./pipeline/Transform/transform_graph/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"transform_graph\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 22\n",
       "        name: \"TransformGraph\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        transformed_examples: Channel(\n",
       "            type_name: Examples\n",
       "            artifacts: [Artifact(artifact: id: 6\n",
       "        type_id: 14\n",
       "        uri: \"./pipeline/Transform/transformed_examples/5\"\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value {\n",
       "            string_value: \"[\\\"train\\\", \\\"eval\\\"]\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"transformed_examples\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 14\n",
       "        name: \"Examples\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        properties {\n",
       "          key: \"version\"\n",
       "          value: INT\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        updated_analyzer_cache: Channel(\n",
       "            type_name: TransformCache\n",
       "            artifacts: [Artifact(artifact: id: 7\n",
       "        type_id: 23\n",
       "        uri: \"./pipeline/Transform/updated_analyzer_cache/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"updated_analyzer_cache\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 23\n",
       "        name: \"TransformCache\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        pre_transform_schema: Channel(\n",
       "            type_name: Schema\n",
       "            artifacts: [Artifact(artifact: id: 8\n",
       "        type_id: 18\n",
       "        uri: \"./pipeline/Transform/pre_transform_schema/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"pre_transform_schema\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 18\n",
       "        name: \"Schema\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        pre_transform_stats: Channel(\n",
       "            type_name: ExampleStatistics\n",
       "            artifacts: [Artifact(artifact: id: 9\n",
       "        type_id: 16\n",
       "        uri: \"./pipeline/Transform/pre_transform_stats/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"pre_transform_stats\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 16\n",
       "        name: \"ExampleStatistics\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        post_transform_schema: Channel(\n",
       "            type_name: Schema\n",
       "            artifacts: [Artifact(artifact: id: 10\n",
       "        type_id: 18\n",
       "        uri: \"./pipeline/Transform/post_transform_schema/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"post_transform_schema\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 18\n",
       "        name: \"Schema\"\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        post_transform_stats: Channel(\n",
       "            type_name: ExampleStatistics\n",
       "            artifacts: [Artifact(artifact: id: 11\n",
       "        type_id: 16\n",
       "        uri: \"./pipeline/Transform/post_transform_stats/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"post_transform_stats\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 16\n",
       "        name: \"ExampleStatistics\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        )\n",
       "        post_transform_anomalies: Channel(\n",
       "            type_name: ExampleAnomalies\n",
       "            artifacts: [Artifact(artifact: id: 12\n",
       "        type_id: 20\n",
       "        uri: \"./pipeline/Transform/post_transform_anomalies/5\"\n",
       "        custom_properties {\n",
       "          key: \"name\"\n",
       "          value {\n",
       "            string_value: \"post_transform_anomalies\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"producer_component\"\n",
       "          value {\n",
       "            string_value: \"Transform\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"state\"\n",
       "          value {\n",
       "            string_value: \"published\"\n",
       "          }\n",
       "        }\n",
       "        custom_properties {\n",
       "          key: \"tfx_version\"\n",
       "          value {\n",
       "            string_value: \"1.3.0\"\n",
       "          }\n",
       "        }\n",
       "        state: LIVE\n",
       "        , artifact_type: id: 20\n",
       "        name: \"ExampleAnomalies\"\n",
       "        properties {\n",
       "          key: \"span\"\n",
       "          value: INT\n",
       "        }\n",
       "        properties {\n",
       "          key: \"split_names\"\n",
       "          value: STRING\n",
       "        }\n",
       "        )]\n",
       "            additional_properties: {}\n",
       "            additional_custom_properties: {}\n",
       "        ))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "### START CODE HERE\n",
    "# Instantiate the Transform component\n",
    "transform = tfx.components.Transform(\n",
    "    examples=example_gen.outputs['examples'],\n",
    "    schema=schema_gen.outputs['schema'],\n",
    "    module_file=os.path.abspath(_traffic_transform_module_file))\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# Run the component.\n",
    "# The `enable_cache` flag is disabled in case you need to update your transform module file.\n",
    "context.run(transform, enable_cache=False)\n",
    "### END CODE HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fwAwb4rARRQ2"
   },
   "source": [
    "You should see the output cell by `InteractiveContext` above and see the three artifacts in `.component.outputs`:\n",
    "\n",
    "* `transform_graph` is the graph that performs the preprocessing operations. This will be included during training and serving to ensure consistent transformations of incoming data.\n",
    "* `transformed_examples` points to the preprocessed training and evaluation data.\n",
    "* `updated_analyzer_cache` are stored calculations from previous runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vyFkBd9AR1sy"
   },
   "source": [
    "The `transform_graph` artifact URI should point to a directory containing:\n",
    "\n",
    "* The `metadata` subdirectory containing the schema of the original data.\n",
    "* The `transformed_metadata` subdirectory containing the schema of the preprocessed data. \n",
    "* The `transform_fn` subdirectory containing the actual preprocessing graph.\n",
    "\n",
    "Again, for grading purposes, we inserted an `except` and `else` below to handle checking the output outside the notebook environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "5tRw4DneR3i7",
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "try:\n",
    "    # Get the uri of the transform graph\n",
    "    transform_graph_uri = transform.outputs['transform_graph'].get()[0].uri\n",
    "\n",
    "except IndexError:\n",
    "    print(\"context.run() was no-op\")\n",
    "    transform_path = './pipeline/Transform/transformed_examples'\n",
    "    dir_id = os.listdir(transform_path)[0]\n",
    "    transform_graph_uri = f'{transform_path}/{dir_id}'\n",
    "    \n",
    "else:\n",
    "    # List the subdirectories under the uri\n",
    "    os.listdir(transform_graph_uri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, you can also take a look at a few of the transformed examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "deletable": false,
    "editable": false,
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "try:\n",
    "    # Get the URI of the output artifact representing the transformed examples\n",
    "    train_uri = os.path.join(transform.outputs['transformed_examples'].get()[0].uri, 'Split-train')\n",
    "    \n",
    "except IndexError:\n",
    "    print(\"context.run() was no-op\")\n",
    "    train_uri = os.path.join(transform_graph_uri, 'Split-train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "pwbW2zPKR_S4",
    "tags": [
     "graded"
    ]
   },
   "outputs": [],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Get the list of files in this directory (all compressed TFRecord files)\n",
    "tfrecord_filenames = [os.path.join(train_uri, name)\n",
    "                      for name in os.listdir(train_uri)]\n",
    "\n",
    "# Create a `TFRecordDataset` to read these files\n",
    "transformed_dataset = tf.data.TFRecordDataset(tfrecord_filenames, compression_type=\"GZIP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "deletable": false,
    "editable": false,
    "id": "mSDZ2rJC7NQW",
    "tags": [
     "graded"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'features': {'feature': {'clouds_all_xf': {'floatList': {'value': [0.4]}},\n",
      "                           'day_of_week_xf': {'int64List': {'value': ['1']}},\n",
      "                           'day_xf': {'int64List': {'value': ['2']}},\n",
      "                           'holiday_xf': {'int64List': {'value': ['0']}},\n",
      "                           'hour_xf': {'int64List': {'value': ['9']}},\n",
      "                           'month_xf': {'int64List': {'value': ['10']}},\n",
      "                           'rain_1h_xf': {'int64List': {'value': ['2']}},\n",
      "                           'snow_1h_xf': {'floatList': {'value': [-0.027424414]}},\n",
      "                           'temp_xf': {'floatList': {'value': [0.53368527]}},\n",
      "                           'traffic_volume_xf': {'int64List': {'value': ['1']}},\n",
      "                           'weather_description_xf': {'int64List': {'value': ['4']}},\n",
      "                           'weather_main_xf': {'int64List': {'value': ['0']}}}}},\n",
      " {'features': {'feature': {'clouds_all_xf': {'floatList': {'value': [0.75]}},\n",
      "                           'day_of_week_xf': {'int64List': {'value': ['1']}},\n",
      "                           'day_xf': {'int64List': {'value': ['2']}},\n",
      "                           'holiday_xf': {'int64List': {'value': ['0']}},\n",
      "                           'hour_xf': {'int64List': {'value': ['10']}},\n",
      "                           'month_xf': {'int64List': {'value': ['10']}},\n",
      "                           'rain_1h_xf': {'int64List': {'value': ['2']}},\n",
      "                           'snow_1h_xf': {'floatList': {'value': [-0.027424414]}},\n",
      "                           'temp_xf': {'floatList': {'value': [0.6156978]}},\n",
      "                           'traffic_volume_xf': {'int64List': {'value': ['1']}},\n",
      "                           'weather_description_xf': {'int64List': {'value': ['3']}},\n",
      "                           'weather_main_xf': {'int64List': {'value': ['0']}}}}},\n",
      " {'features': {'feature': {'clouds_all_xf': {'floatList': {'value': [0.9]}},\n",
      "                           'day_of_week_xf': {'int64List': {'value': ['1']}},\n",
      "                           'day_xf': {'int64List': {'value': ['2']}},\n",
      "                           'holiday_xf': {'int64List': {'value': ['0']}},\n",
      "                           'hour_xf': {'int64List': {'value': ['11']}},\n",
      "                           'month_xf': {'int64List': {'value': ['10']}},\n",
      "                           'rain_1h_xf': {'int64List': {'value': ['2']}},\n",
      "                           'snow_1h_xf': {'floatList': {'value': [-0.027424414]}},\n",
      "                           'temp_xf': {'floatList': {'value': [0.6324044]}},\n",
      "                           'traffic_volume_xf': {'int64List': {'value': ['1']}},\n",
      "                           'weather_description_xf': {'int64List': {'value': ['2']}},\n",
      "                           'weather_main_xf': {'int64List': {'value': ['0']}}}}}]\n"
     ]
    }
   ],
   "source": [
    "# grader-required-cell\n",
    "\n",
    "# Get 3 records from the dataset\n",
    "sample_records_xf = get_records(transformed_dataset, 3)\n",
    "\n",
    "# Print the output\n",
    "pp.pprint(sample_records_xf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Congratulations on completing this week's assignment!** You've just demonstrated how to build a data pipeline and do feature engineering. You will build upon these concepts in the next weeks where you will deal with more complex datasets and also access the metadata store. Keep up the good work!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "  <summary><font size=\"2\" color=\"darkgreen\"><b>Please click here if you want to experiment with any of the non-graded code.</b></font></summary>\n",
    "    <p><i><b>Important Note: Please only do this when you've already passed the assignment to avoid problems with the autograder.</b></i>\n",
    "    <ol>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “Edit Metadata”</li>\n",
    "        <li> Hit the “Edit Metadata” button next to the code cell which you want to lock/unlock</li>\n",
    "        <li> Set the attribute value for “editable” to:\n",
    "            <ul>\n",
    "                <li> “true” if you want to unlock it </li>\n",
    "                <li> “false” if you want to lock it </li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li> On the notebook’s menu, click “View” > “Cell Toolbar” > “None” </li>\n",
    "    </ol>\n",
    "    <p> Here's a short demo of how to do the steps above: \n",
    "        <br>\n",
    "        <img src=\"https://drive.google.com/uc?export=view&id=14Xy_Mb17CZVgzVAgq7NCjMVBvSae3xO1\" align=\"center\">\n",
    "</details>"
   ]
  }
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