notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "copyright"
      },
      "outputs": [],
      "source": [
        "# Copyright 2021 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "title"
      },
      "source": [
        "# Vertex AI SDK: AutoML training tabular binary classification model for batch explanation\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>\n",
        "</table>\n",
        "<br/><br/><br/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "overview:automl,xai"
      },
      "source": [
        "## Overview\n",
        "\n",
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK to create tabular binary classification models and do batch prediction with explanation using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model.\n",
        "\n",
        "Learn more about [Classification for tabular data](https://cloud.google.com/vertex-ai/docs/tabular-data/classification-regression/overview). Learn more about [Vertex Explainable AI](https://cloud.google.com/vertex-ai/docs/explainable-ai/overview)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "objective:automl,training,batch_prediction,xai"
      },
      "source": [
        "### Objective\n",
        "\n",
        "In this tutorial, you learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Batch Prediction` to make predictions with explanations. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
        "\n",
        "This tutorial uses the following Google Cloud ML services:\n",
        "\n",
        "- `Vertex AI AutoML`\n",
        "- `Vertex AI Batch Prediction`\n",
        "- `Vertex Explainable AI`\n",
        "- `Vertex AI Model` resource\n",
        "\n",
        "\n",
        "The steps performed include:\n",
        "\n",
        "- Create a `Vertex Dataset` resource.\n",
        "- Train an `AutoML` tabular binary classification model.\n",
        "- View the model evaluation metrics for the trained model.\n",
        "- Make a batch prediction request with explainability.\n",
        "\n",
        "There is one key difference between using batch prediction and using online prediction:\n",
        "\n",
        "* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.\n",
        "\n",
        "* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a4881cf39a4"
      },
      "source": [
        "### Dataset\n",
        "\n",
        "The dataset used for this tutorial is the Bank Marketing. This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "costs"
      },
      "source": [
        "### Costs\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI\n",
        "* Cloud Storage\n",
        "\n",
        "Learn about [Vertex AI\n",
        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
        "Calculator](https://cloud.google.com/products/calculator/)\n",
        "to generate a cost estimate based on your projected usage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "install_aip:mbsdk"
      },
      "source": [
        "## Installation\n",
        "\n",
        "Install the packages required for executing this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "install_aip:mbsdk"
      },
      "outputs": [],
      "source": [
        "! pip3 install --upgrade --quiet google-cloud-aiplatform\n",
        "! pip3 install --upgrade --quiet google-cloud-storage\n",
        "! pip3 install --upgrade --quiet tensorflow"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "restart"
      },
      "source": [
        "### Colab only: Uncomment the following cell to restart the kernel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-ZBOjErv5mM"
      },
      "outputs": [],
      "source": [
        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
        "# import IPython\n",
        "\n",
        "# app = IPython.Application.instance()\n",
        "# app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfEglUHQk9S3"
      },
      "source": [
        "## Before you begin\n",
        "\n",
        "### Set your project ID\n",
        "\n",
        "**If you don't know your project ID**, try the following:\n",
        "* Run `gcloud config list`.\n",
        "* Run `gcloud projects list`.\n",
        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "set_project_id"
      },
      "outputs": [],
      "source": [
        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
        "\n",
        "# Set the project id\n",
        "! gcloud config set project {PROJECT_ID}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "region"
      },
      "source": [
        "#### Region\n",
        "\n",
        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "region"
      },
      "outputs": [],
      "source": [
        "REGION = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcp_authenticate"
      },
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
        "\n",
        "**1. Vertex AI Workbench**\n",
        "* Do nothing as you are already authenticated.\n",
        "\n",
        "**2. Local JupyterLab instance, uncomment and run:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ce6043da7b33"
      },
      "outputs": [],
      "source": [
        "# ! gcloud auth login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0367eac06a10"
      },
      "source": [
        "**3. Colab, uncomment and run:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21ad4dbb4a61"
      },
      "outputs": [],
      "source": [
        "# from google.colab import auth\n",
        "# auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c13224697bfb"
      },
      "source": [
        "**4. Service account or other**\n",
        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bucket:mbsdk"
      },
      "source": [
        "### Create a Cloud Storage bucket\n",
        "\n",
        "Create a storage bucket to store intermediate artifacts such as datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bucket"
      },
      "outputs": [],
      "source": [
        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "create_bucket"
      },
      "source": [
        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "create_bucket"
      },
      "outputs": [],
      "source": [
        "! gsutil mb -l $REGION $BUCKET_URI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "setup_vars"
      },
      "source": [
        "### Set up variables\n",
        "\n",
        "Next, set up some variables used throughout the tutorial.\n",
        "### Import libraries and define constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "import_aip:mbsdk"
      },
      "outputs": [],
      "source": [
        "import google.cloud.aiplatform as aip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "init_aip:mbsdk"
      },
      "source": [
        "### Initialize Vertex AI SDK for Python\n",
        "\n",
        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "init_aip:mbsdk"
      },
      "outputs": [],
      "source": [
        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tutorial_start:automl"
      },
      "source": [
        "# Tutorial\n",
        "\n",
        "Now you are ready to start creating your own AutoML tabular binary classification model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "import_file:u_dataset,csv"
      },
      "source": [
        "#### Location of Cloud Storage training data.\n",
        "\n",
        "Now set the variable `IMPORT_FILE` to the location of the CSV index file in Cloud Storage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "import_file:bank,csv,lbn"
      },
      "outputs": [],
      "source": [
        "IMPORT_FILE = \"gs://cloud-ml-tables-data/bank-marketing.csv\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "quick_peek:tabular"
      },
      "source": [
        "#### Quick peek at your data\n",
        "\n",
        "You use a version of the Bank Marketing dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
        "\n",
        "Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file  (`wc -l`) and then peek at the first few rows.\n",
        "\n",
        "You also need for training to know the heading name of the label column, which is save as `label_column`. For this dataset, it is the last column in the CSV file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "quick_peek:tabular"
      },
      "outputs": [],
      "source": [
        "count = ! gsutil cat $IMPORT_FILE | wc -l\n",
        "print(\"Number of Examples\", int(count[0]))\n",
        "\n",
        "print(\"First 10 rows\")\n",
        "! gsutil cat $IMPORT_FILE | head\n",
        "\n",
        "heading = ! gsutil cat $IMPORT_FILE | head -n1\n",
        "label_column = str(heading).split(\",\")[-1].split(\"'\")[0]\n",
        "print(\"Label Column Name\", label_column)\n",
        "if label_column is None:\n",
        "    raise Exception(\"label column missing\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "create_dataset:tabular,lbn"
      },
      "source": [
        "### Create the Dataset\n",
        "\n",
        "Next, create the `Dataset` resource using the `create` method for the `TabularDataset` class, which takes the following parameters:\n",
        "\n",
        "- `display_name`: The human readable name for the `Dataset` resource.\n",
        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
        "- `bq_source`: Alternatively, import data items from a BigQuery table into the `Dataset` resource.\n",
        "\n",
        "This operation may take several minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "create_dataset:tabular,lbn"
      },
      "outputs": [],
      "source": [
        "dataset = aip.TabularDataset.create(\n",
        "    display_name=\"Bank Marketing\", gcs_source=[IMPORT_FILE]\n",
        ")\n",
        "\n",
        "print(dataset.resource_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "create_automl_pipeline:tabular,lbn"
      },
      "source": [
        "### Create and run training pipeline\n",
        "\n",
        "To train an AutoML model, you perform two steps: 1) create a training pipeline, and 2) run the pipeline.\n",
        "\n",
        "#### Create training pipeline\n",
        "\n",
        "An AutoML training pipeline is created with the `AutoMLTabularTrainingJob` class, with the following parameters:\n",
        "\n",
        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
        "- `optimization_prediction_type`: The type task to train the model for.\n",
        "  - `classification`: A tabuar classification model.\n",
        "  - `regression`: A tabular regression model.\n",
        "- `column_transformations`: (Optional): Transformations to apply to the input columns\n",
        "- `optimization_objective`: The optimization objective to minimize or maximize.\n",
        "  - binary classification:\n",
        "    - `minimize-log-loss`\n",
        "    - `maximize-au-roc`\n",
        "    - `maximize-au-prc`\n",
        "    - `maximize-precision-at-recall`\n",
        "    - `maximize-recall-at-precision`\n",
        "  - multi-class classification:\n",
        "    - `minimize-log-loss`\n",
        "  - regression:\n",
        "    - `minimize-rmse`\n",
        "    - `minimize-mae`\n",
        "    - `minimize-rmsle`\n",
        "\n",
        "The instantiated object is the DAG (directed acyclic graph) for the training pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "create_automl_pipeline:tabular,lbn"
      },
      "outputs": [],
      "source": [
        "dag = aip.AutoMLTabularTrainingJob(\n",
        "    display_name=\"bank\",\n",
        "    optimization_prediction_type=\"classification\",\n",
        "    optimization_objective=\"minimize-log-loss\",\n",
        ")\n",
        "\n",
        "print(dag)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "run_automl_pipeline:tabular"
      },
      "source": [
        "#### Run the training pipeline\n",
        "\n",
        "Next, you run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
        "\n",
        "- `dataset`: The `Dataset` resource to train the model.\n",
        "- `model_display_name`: The human readable name for the trained model.\n",
        "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
        "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
        "- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
        "- `target_column`: The name of the column to train as the label.\n",
        "- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
        "- `disable_early_stopping`: If `True`, training maybe completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
        "\n",
        "The `run` method when completed returns the `Model` resource.\n",
        "\n",
        "The execution of the training pipeline will take upto 20 minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "run_automl_pipeline:tabular"
      },
      "outputs": [],
      "source": [
        "model = dag.run(\n",
        "    dataset=dataset,\n",
        "    model_display_name=\"bank\",\n",
        "    training_fraction_split=0.6,\n",
        "    validation_fraction_split=0.2,\n",
        "    test_fraction_split=0.2,\n",
        "    budget_milli_node_hours=8000,\n",
        "    disable_early_stopping=False,\n",
        "    target_column=label_column,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evaluate_the_model:mbsdk"
      },
      "source": [
        "## Review model evaluation scores\n",
        "\n",
        "After your model training has finished, you can review the evaluation scores for it using the `list_model_evaluations()` method. This method will return an iterator for each evaluation slice."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "evaluate_the_model:mbsdk"
      },
      "outputs": [],
      "source": [
        "model_evaluations = model.list_model_evaluations()\n",
        "\n",
        "for model_evaluation in model_evaluations:\n",
        "    print(model_evaluation.to_dict())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "make_prediction"
      },
      "source": [
        "## Send a batch prediction request\n",
        "\n",
        "Send a batch prediction to your deployed model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "make_test_items:automl,batch_prediction"
      },
      "source": [
        "### Make test items\n",
        "\n",
        "You use synthetic data as a test data items. Don't be concerned that we are using synthetic data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "make_batch_file:automl,tabular,alt"
      },
      "source": [
        "### Make the batch input file\n",
        "\n",
        "Now make a batch input file, which you will store in your local Cloud Storage bucket. Unlike image, video and text, the batch input file for tabular is only supported for CSV. For CSV file, you make:\n",
        "\n",
        "- The first line is the heading with the feature (fields) heading names.\n",
        "- Each remaining line is a separate prediction request with the corresponding feature values.\n",
        "\n",
        "For example:\n",
        "\n",
        "    \"feature_1\", \"feature_2\". ...\n",
        "    value_1, value_2, ..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "make_batch_file:automl,tabular,alt"
      },
      "outputs": [],
      "source": [
        "! gsutil cat $IMPORT_FILE | head -n 1 > tmp.csv\n",
        "! gsutil cat $IMPORT_FILE | tail -n 10 >> tmp.csv\n",
        "\n",
        "! cut -d, -f1-16 tmp.csv > batch.csv\n",
        "\n",
        "gcs_input_uri = BUCKET_URI + \"/test.csv\"\n",
        "\n",
        "! gsutil cp batch.csv $gcs_input_uri"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batch_explain_request:mbsdk,csv"
      },
      "source": [
        "### Make the batch explanation request\n",
        "\n",
        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
        "\n",
        "- `job_display_name`: The human readable name for the batch prediction job.\n",
        "- `gcs_source`: A list of one or more batch request input files.\n",
        "- `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
        "- `instances_format`: The format for the input instances, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
        "- `predictions_format`: The format for the output predictions, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
        "- `generate_explanations`: Set to `True` to generate explanations.\n",
        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "batch_explain_request:mbsdk,csv"
      },
      "outputs": [],
      "source": [
        "batch_predict_job = model.batch_predict(\n",
        "    job_display_name=\"bank\",\n",
        "    gcs_source=gcs_input_uri,\n",
        "    gcs_destination_prefix=BUCKET_URI,\n",
        "    instances_format=\"csv\",\n",
        "    predictions_format=\"jsonl\",\n",
        "    generate_explanation=True,\n",
        "    sync=False,\n",
        ")\n",
        "\n",
        "print(batch_predict_job)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batch_request_wait:mbsdk"
      },
      "source": [
        "### Wait for completion of batch prediction job\n",
        "\n",
        "Next, wait for the batch job to complete. Alternatively, one can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "batch_request_wait:mbsdk"
      },
      "outputs": [],
      "source": [
        "batch_predict_job.wait()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "get_batch_explanation:mbsdk,lbn"
      },
      "source": [
        "### Get the explanations\n",
        "\n",
        "Next, get the explanation results from the completed batch prediction job.\n",
        "\n",
        "The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more explanation requests in a CSV format:\n",
        "\n",
        "- CSV header + predicted_label\n",
        "- CSV row + explanation, per prediction request"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "get_batch_explanation:mbsdk,lbn"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "bp_iter_outputs = batch_predict_job.iter_outputs()\n",
        "\n",
        "explanation_results = list()\n",
        "for blob in bp_iter_outputs:\n",
        "    if blob.name.split(\"/\")[-1].startswith(\"explanation\"):\n",
        "        explanation_results.append(blob.name)\n",
        "\n",
        "tags = list()\n",
        "for explanation_result in explanation_results:\n",
        "    gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{explanation_result}\"\n",
        "    with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
        "        for line in gfile.readlines():\n",
        "            print(line)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cleanup:mbsdk"
      },
      "source": [
        "# Cleaning up\n",
        "\n",
        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
        "\n",
        "Otherwise, you can delete the individual resources you created in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cleanup:mbsdk"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# Set this to true only if you'd like to delete your bucket\n",
        "delete_bucket = False\n",
        "\n",
        "dataset.delete()\n",
        "model.delete()\n",
        "batch_predict_job.delete()\n",
        "\n",
        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
        "    ! gsutil rm -r $BUCKET_URI"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "sdk_automl_tabular_binary_classification_batch_explain.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}