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Roope Astala
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automl/10.auto-ml-multi-output-example.ipynb
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286
automl/10.auto-ml-multi-output-example.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# AutoML 10: Multi output Example for AutoML"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook shows an example to use AutoML to train the multi output problems by leveraging the correlation between the outputs using indicator vectors."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"import os\n",
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"import random\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"from matplotlib.pyplot import imshow\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"from sklearn import datasets\n",
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"\n",
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"import azureml.core\n",
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"from azureml.core.experiment import Experiment\n",
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"from azureml.core.workspace import Workspace\n",
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"from azureml.train.automl import AutoMLConfig\n",
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"from azureml.train.automl.run import AutoMLRun"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Diagnostics\n",
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"\n",
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"Opt-in diagnostics for better experience, quality, and security of future releases"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.telemetry import set_diagnostics_collection\n",
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"set_diagnostics_collection(send_diagnostics=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Transformer functions\n",
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"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",
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" \n",
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"X 1 0\n",
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" \n",
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"X 0 1\n",
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"\n",
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"and Y becomes,\n",
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"\n",
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"y_1\n",
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"\n",
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"y_2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from scipy import sparse\n",
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"from scipy import linalg\n",
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"\n",
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"#Transformer functions\n",
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"def multi_output_transform_x_y(X, Y):\n",
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" X_new = multi_output_transformer_x(X, Y.shape[1])\n",
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" y_new = multi_output_transform_y(Y)\n",
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" return X_new, y_new\n",
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"\n",
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"def multi_output_transformer_x(X, number_of_columns_Y):\n",
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" indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_Y))\n",
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" if sparse.issparse(X):\n",
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" X_new = sparse.vstack(np.tile(X, number_of_columns_Y))\n",
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" indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
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" X_new = sparse.hstack((X_new, indicator_vecs))\n",
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" else:\n",
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" X_new = np.tile(X, (number_of_columns_Y, 1))\n",
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" X_new = np.hstack((X_new, indicator_vecs))\n",
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" return X_new\n",
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"\n",
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"def multi_output_transform_y(Y):\n",
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" return Y.reshape(-1, order=\"F\")\n",
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" \n",
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"def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
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" return y.reshape((-1, number_of_columns_y), order=\"F\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## AutoML experiment set up"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"ws = Workspace.from_config()\n",
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"\n",
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"# choose a name for experiment\n",
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"experiment_name = 'automl-local-classification'\n",
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"# project folder\n",
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"project_folder = './sample_projects/automl-local-classification'\n",
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"\n",
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"experiment=Experiment(ws, experiment_name)\n",
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"\n",
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"output = {}\n",
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"output['SDK version'] = azureml.core.VERSION\n",
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"output['Subscription ID'] = ws.subscription_id\n",
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"output['Workspace'] = ws.name\n",
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"output['Resource Group'] = ws.resource_group\n",
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"output['Location'] = ws.location\n",
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"output['Project Directory'] = project_folder\n",
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"output['Experiment Name'] = experiment.name\n",
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"pd.set_option('display.max_colwidth', -1)\n",
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"pd.DataFrame(data=output, index=['']).T"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Create a random dataset for the test purpose "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"rng = np.random.RandomState(1)\n",
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"X_train = np.sort(200 * rng.rand(600, 1) - 100, axis=0)\n",
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"Y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
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"Y_train += (0.5 - rng.rand(*Y_train.shape))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Perform X and Y transformation using transformer function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, Y_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"automl_config = AutoMLConfig(task = 'regression',\n",
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" debug_log='automl_errors_multi.log',\n",
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" primary_metric='r2_score',\n",
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" iterations=10,\n",
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" n_cross_validations=2,\n",
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" verbosity=logging.INFO,\n",
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" X=X_train_transformed,\n",
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" y=y_train_transformed,\n",
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" path=project_folder)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Fit the transformed data "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"local_run = experiment.submit(automl_config, show_output=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the best fit model\n",
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"best_run, fitted_model = local_run.get_output()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Generate random data set for predicting\n",
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"X_predict = np.sort(200 * rng.rand(200, 1) - 100, axis=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Transform predict data\n",
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"X_predict_transformed = multi_output_transformer_x(X_predict, Y_train.shape[1])\n",
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"# Predict and inverse transform the prediction\n",
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"y_predict = fitted_model.predict(X_predict_transformed)\n",
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"Y_predict = multi_output_inverse_transform_y(y_predict, Y_train.shape[1])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(Y_predict)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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