update notebooks for new version

automl/10.auto-ml-multi-output-example.ipynb

@@ -1,292 +1,287 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-    "\n",
-    "Licensed under the MIT License."
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+        "\n",
+        "Licensed under the MIT License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# AutoML 10: Multi-output\n",
+        "\n",
+        "This notebook shows how to use AutoML to train multi-output problems by leveraging the correlation between the outputs using indicator vectors."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import logging\n",
+        "import os\n",
+        "import random\n",
+        "\n",
+        "from matplotlib import pyplot as plt\n",
+        "from matplotlib.pyplot import imshow\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",
+        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.train.automl.run import AutoMLRun"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Diagnostics\n",
+        "\n",
+        "Opt-in diagnostics for better experience, quality, and security of future releases."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.telemetry import set_diagnostics_collection\n",
+        "set_diagnostics_collection(send_diagnostics = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Transformer Functions\n",
+        "The transformations of inputs `X` and `y` are happening as follows, e.g. `y = {y_1, y_2}`, then `X` becomes\n",
+        "    \n",
+        "`X 1 0`\n",
+        "     \n",
+        "`X 0 1`\n",
+        "\n",
+        "and `y` becomes,\n",
+        "\n",
+        "`y_1`\n",
+        "\n",
+        "`y_2`"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from scipy import sparse\n",
+        "from scipy import linalg\n",
+        "\n",
+        "#Transformer functions\n",
+        "def multi_output_transform_x_y(X, y):\n",
+        "    X_new = multi_output_transformer_x(X, y.shape[1])\n",
+        "    y_new = multi_output_transform_y(y)\n",
+        "    return X_new, y_new\n",
+        "\n",
+        "def multi_output_transformer_x(X, number_of_columns_y):\n",
+        "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_y))\n",
+        "    if sparse.issparse(X):\n",
+        "        X_new = sparse.vstack(np.tile(X, number_of_columns_y))\n",
+        "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
+        "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
+        "    else:\n",
+        "        X_new = np.tile(X, (number_of_columns_y, 1))\n",
+        "        X_new = np.hstack((X_new, indicator_vecs))\n",
+        "    return X_new\n",
+        "\n",
+        "def multi_output_transform_y(y):\n",
+        "    return y.reshape(-1, order=\"F\")\n",
+        "\n",
+        "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
+        "    return y.reshape((-1, number_of_columns_y), order = \"F\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## AutoML Experiment Setup"
+      ]
+    },
+    {
+      "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-multi-output'\n",
+        "project_folder = './sample_projects/automl-local-multi-output'\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'] = 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": [
+        "## Create a Random Dataset for Test Purposes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "rng = np.random.RandomState(1)\n",
+        "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis = 0)\n",
+        "y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
+        "y_train += (0.5 - rng.rand(*y_train.shape))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Perform X and y transformation using the transformer function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Configure AutoML using the transformed results."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "automl_config = AutoMLConfig(task = 'regression',\n",
+        "                             debug_log = 'automl_errors_multi.log',\n",
+        "                             primary_metric = 'r2_score',\n",
+        "                             iterations = 10,\n",
+        "                             n_cross_validations = 2,\n",
+        "                             verbosity = logging.INFO,\n",
+        "                             X = X_train_transformed,\n",
+        "                             y = y_train_transformed,\n",
+        "                             path = project_folder)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Fit the Transformed Data"
+      ]
+    },
+    {
+      "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": [
+        "# Get the best fit model.\n",
+        "best_run, fitted_model = local_run.get_output()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Generate random data set for predicting.\n",
+        "X_test = np.sort(200 * rng.rand(200, 1) - 100, axis = 0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Transform predict data.\n",
+        "X_test_transformed = multi_output_transformer_x(X_test, y_train.shape[1])\n",
+        "\n",
+        "# Predict and inverse transform the prediction.\n",
+        "y_predict = fitted_model.predict(X_test_transformed)\n",
+        "y_predict = multi_output_inverse_transform_y(y_predict, y_train.shape[1])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(y_predict)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3.6",
+      "language": "python",
+      "name": "python36"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.6"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AutoML 10: Multi output Example for AutoML"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This notebook shows an example to use AutoML to train the multi output problems by leveraging the correlation between the outputs using indicator vectors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "import random\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from matplotlib.pyplot import imshow\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",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.train.automl.run import AutoMLRun"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Diagnostics\n",
-    "\n",
-    "Opt-in diagnostics for better experience, quality, and security of future releases"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from azureml.telemetry import set_diagnostics_collection\n",
-    "set_diagnostics_collection(send_diagnostics=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transformer functions\n",
-    "The transformation of the input are happening for input X and Y as following, e.g. Y = {y_1, y_2}, then X becomes\n",
-    "    \n",
-    "X 1 0\n",
-    "     \n",
-    "X 0 1\n",
-    "\n",
-    "and Y becomes,\n",
-    "\n",
-    "y_1\n",
-    "\n",
-    "y_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import sparse\n",
-    "from scipy import linalg\n",
-    "\n",
-    "#Transformer functions\n",
-    "def multi_output_transform_x_y(X, Y):\n",
-    "    X_new = multi_output_transformer_x(X, Y.shape[1])\n",
-    "    y_new = multi_output_transform_y(Y)\n",
-    "    return X_new, y_new\n",
-    "\n",
-    "def multi_output_transformer_x(X, number_of_columns_Y):\n",
-    "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_Y))\n",
-    "    if sparse.issparse(X):\n",
-    "        X_new = sparse.vstack(np.tile(X, number_of_columns_Y))\n",
-    "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
-    "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
-    "    else:\n",
-    "        X_new = np.tile(X, (number_of_columns_Y, 1))\n",
-    "        X_new = np.hstack((X_new, indicator_vecs))\n",
-    "    return X_new\n",
-    "\n",
-    "def multi_output_transform_y(Y):\n",
-    "    return Y.reshape(-1, order=\"F\")\n",
-    "    \n",
-    "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
-    "    return y.reshape((-1, number_of_columns_y), order=\"F\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## AutoML experiment set up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "\n",
-    "# choose a name for experiment\n",
-    "experiment_name = 'automl-local-multi-output'\n",
-    "# project folder\n",
-    "project_folder = './sample_projects/automl-local-multi-output'\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'] = 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": [
-    "## Create a random dataset for the test purpose "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rng = np.random.RandomState(1)\n",
-    "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis=0)\n",
-    "Y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
-    "Y_train += (0.5 - rng.rand(*Y_train.shape))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Perform X and Y transformation using transformer function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, Y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "automl_config = AutoMLConfig(task = 'regression',\n",
-    "                             debug_log='automl_errors_multi.log',\n",
-    "                             primary_metric='r2_score',\n",
-    "                             iterations=10,\n",
-    "                             n_cross_validations=2,\n",
-    "                             verbosity=logging.INFO,\n",
-    "                             X=X_train_transformed,\n",
-    "                             y=y_train_transformed,\n",
-    "                             path=project_folder)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fit the transformed data "
-   ]
-  },
-  {
-   "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": [
-    "# Get the best fit model\n",
-    "best_run, fitted_model = local_run.get_output()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate random data set for predicting\n",
-    "X_predict = np.sort(200 * rng.rand(200, 1) - 100, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Transform predict data\n",
-    "X_predict_transformed = multi_output_transformer_x(X_predict, Y_train.shape[1])\n",
-    "# Predict and inverse transform the prediction\n",
-    "y_predict = fitted_model.predict(X_predict_transformed)\n",
-    "Y_predict = multi_output_inverse_transform_y(y_predict, Y_train.shape[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(Y_predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.6",
-   "language": "python",
-   "name": "python36"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 2
+}