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349 lines
11 KiB
Plaintext
349 lines
11 KiB
Plaintext
{
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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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""
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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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"# Automated Machine Learning\n",
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"_**Exploring Previous Runs**_\n",
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"\n",
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"## Contents\n",
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"1. [Introduction](#Introduction)\n",
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"1. [Setup](#Setup)\n",
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"1. [Explore](#Explore)\n",
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"1. [Download](#Download)\n",
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"1. [Register](#Register)"
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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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"## Introduction\n",
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"In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
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"\n",
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"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
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"\n",
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"In this notebook you will learn how to:\n",
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"1. List all experiments in a workspace.\n",
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"2. List all AutoML runs in an experiment.\n",
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"3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
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"4. Download a fitted pipeline for any iteration."
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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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"## Setup"
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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 pandas as pd\n",
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"import json\n",
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"\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.run import AutoMLRun"
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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()"
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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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"## Explore"
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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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"### List Experiments"
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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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"experiment_list = Experiment.list(workspace=ws)\n",
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"\n",
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"summary_df = pd.DataFrame(index = ['No of Runs'])\n",
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"for experiment in experiment_list:\n",
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" automl_runs = list(experiment.get_runs(type='automl'))\n",
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" summary_df[experiment.name] = [len(automl_runs)]\n",
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" \n",
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"pd.set_option('display.max_colwidth', -1)\n",
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"summary_df.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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"### List runs for an experiment\n",
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"Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
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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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"experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
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"\n",
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"proj = ws.experiments[experiment_name]\n",
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"summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
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"automl_runs = list(proj.get_runs(type='automl'))\n",
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"automl_runs_project = []\n",
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"for run in automl_runs:\n",
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" properties = run.get_properties()\n",
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" tags = run.get_tags()\n",
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" amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
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" if 'iterations' in tags:\n",
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" iterations = tags['iterations']\n",
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" else:\n",
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" iterations = properties['num_iterations']\n",
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" summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
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" if run.get_details()['status'] == 'Completed':\n",
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" automl_runs_project.append(run.id)\n",
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" \n",
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"from IPython.display import HTML\n",
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"projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
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"\n",
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"from IPython.display import display\n",
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"display(projname_html)\n",
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"display(summary_df.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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"### Get details for a run\n",
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"\n",
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"Copy the project name and run id from the previous cell output to find more details on a particular run."
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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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"run_id = automl_runs_project[0] # Replace with your own run_id from above run ids\n",
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"assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
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"\n",
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"from azureml.widgets import RunDetails\n",
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"\n",
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"experiment = Experiment(ws, experiment_name)\n",
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"ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
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"\n",
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"summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
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"properties = ml_run.get_properties()\n",
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"tags = ml_run.get_tags()\n",
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"status = ml_run.get_details()\n",
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"amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
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"if 'iterations' in tags:\n",
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" iterations = tags['iterations']\n",
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"else:\n",
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" iterations = properties['num_iterations']\n",
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"start_time = None\n",
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"if 'startTimeUtc' in status:\n",
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" start_time = status['startTimeUtc']\n",
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"end_time = None\n",
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"if 'endTimeUtc' in status:\n",
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" end_time = status['endTimeUtc']\n",
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"summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
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"display(HTML('<h3>Runtime Details</h3>'))\n",
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"display(summary_df)\n",
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"\n",
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"#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
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"display(HTML('<h3>AutoML Settings</h3>'))\n",
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"display(amlsettings)\n",
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"\n",
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"display(HTML('<h3>Iterations</h3>'))\n",
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"RunDetails(ml_run).show() \n",
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"\n",
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"children = list(ml_run.get_children())\n",
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"metricslist = {}\n",
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"for run in children:\n",
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" properties = run.get_properties()\n",
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" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
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" metricslist[int(properties['iteration'])] = metrics\n",
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"\n",
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"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
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"display(HTML('<h3>Metrics</h3>'))\n",
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"display(rundata)\n"
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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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"## Download"
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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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"### Download the Best Model for Any Given Metric"
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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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"metric = 'AUC_weighted' # Replace with a metric name.\n",
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"best_run, fitted_model = ml_run.get_output(metric = metric)\n",
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"fitted_model"
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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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"### Download the Model for Any Given Iteration"
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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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"iteration = 1 # Replace with an iteration number.\n",
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"best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
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"fitted_model"
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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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"## Register"
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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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"### Register fitted model for deployment\n",
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"If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
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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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"description = 'AutoML Model'\n",
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"tags = None\n",
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"ml_run.register_model(description = description, tags = tags)\n",
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"print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
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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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"### Register the Best Model for Any Given Metric"
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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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"metric = 'AUC_weighted' # Replace with a metric name.\n",
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"description = 'AutoML Model'\n",
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"tags = None\n",
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"ml_run.register_model(description = description, tags = tags, metric = metric)\n",
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"print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
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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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"### Register the Model for Any Given Iteration"
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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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"iteration = 1 # Replace with an iteration number.\n",
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"description = 'AutoML Model'\n",
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"tags = None\n",
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"ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
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"print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
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]
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}
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],
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"metadata": {
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"authors": [
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{
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"name": "savitam"
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}
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],
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
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"name": "python36"
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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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"nbformat": 4,
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} |