MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/subsampling/auto-ml-subsampling-local.ipynb

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        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
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        "# Automated Machine Learning\n",
        "_**Classification with Local Compute**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we will explore AutoML's subsampling feature. This is useful for training on large datasets to speed up the convergence.\n",
        "\n",
        "The setup is quiet similar to a normal classification, with the exception of the `enable_subsampling` option. Keep in mind that even with the `enable_subsampling` flag set, subsampling will only be run for large datasets (>= 50k rows) and large (>= 85) or no iteration restrictions.\n"
      ]
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      "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,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment and specify the project folder.\n",
        "experiment_name = 'automl-subsampling'\n",
        "project_folder = './sample_projects/automl-subsampling'\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",
        "pd.DataFrame(data = output, index = ['']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "\n",
        "We will create a simple dataset using the numpy sin function just for this example. We need just over 50k rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "base = np.arange(60000)\n",
        "cos = np.cos(base)\n",
        "y = np.round(np.sin(base)).astype('int')\n",
        "\n",
        "# Exclude the first 100 rows from training so that they can be used for test.\n",
        "X_train = np.hstack((base.reshape(-1, 1), cos.reshape(-1, 1)))\n",
        "y_train = y"
      ]
    },
    {
      "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",
        "|**enable_subsampling**|This enables subsampling as an option. However it does not guarantee subsampling will be used. It also depends on how large the dataset is and how many iterations it's expected to run at a minimum.|\n",
        "|**iterations**|Number of iterations. Subsampling requires a lot of iterations at smaller percent so in order for subsampling to be used we need to set iterations to be a high number.|\n",
        "|**experiment_timeout_minutes**|The experiment timeout, it's set to 5 right now to shorten the demo but it should probably be higher if we want to finish all the iterations.|\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'accuracy',\n",
        "                             iterations = 85,\n",
        "                             experiment_timeout_minutes = 5,\n",
        "                             n_cross_validations = 2,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             enable_subsampling=True,\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": [],
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    "authors": [
      {
        "name": "rogehe"
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