MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb

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      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
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      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.png)"
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      "source": [
        "# Automated Machine Learning\n",
        "_**Classification using whitelist models**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
        "This trains the model exclusively on tensorflow based models.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model on a whilelisted models using local compute. \n",
        "4. Explore the results.\n",
        "5. Test the best fitted model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "#Note: This notebook will install tensorflow if not already installed in the enviornment..\n",
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "import sys\n",
        "whitelist_models=[\"LightGBM\"]\n",
        "if \"3.7\" != sys.version[0:3]:\n",
        "    try:\n",
        "        import tensorflow as tf1\n",
        "    except ImportError:\n",
        "        from pip._internal import main\n",
        "        main(['install', 'tensorflow>=1.10.0,<=1.12.0'])\n",
        "        logging.getLogger().setLevel(logging.ERROR)\n",
        "    whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"]\n",
        "\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment and specify the project folder.\n",
        "experiment_name = 'automl-local-whitelist'\n",
        "project_folder = './sample_projects/automl-local-whitelist'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Project Directory'] = project_folder\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "\n",
        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "\n",
        "# Exclude the first 100 rows from training so that they can be used for test.\n",
        "X_train = digits.data[100:,:]\n",
        "y_train = digits.target[100:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
        "|**whitelist_models**|List of models that AutoML should use.  The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 10,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             enable_tf=True,\n",
        "                             whitelist_models=whitelist_models,\n",
        "                             path = project_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the third iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 3\n",
        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
        "print(third_run)\n",
        "print(third_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will try to predict 2 digits and see how our model works."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    }
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