jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
mirror of https://github.com/Azure/MachineLearningNotebooks.git synced 2025-12-22 02:25:12 -05:00
Code Issues Projects Releases Wiki Activity

Update 12/2

Browse Source
This commit is contained in:
rastala
2018-12-02 14:46:52 -05:00
parent 74309f91f7
commit 13a5d0baac
26 changed files with 87295 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
configuration.ipynb
View File

@@ -96,7 +96,7 @@
"source": [ "source": [
"import azureml.core\n", "import azureml.core\n",
"\n", "\n",
"print(\"This notebook was created using version AZUREML-SDK-VERSION of the Azure ML SDK\")\n", "print(\"This notebook was created using version 1.0.2 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
] ]
}, },

61
how-to-use-azureml/automated-machine-learning/README.md
View File

@@ -25,7 +25,7 @@ Below are the three execution environments supported by AutoML.
1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks) 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks. [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
1. Follow the instructions in the [../00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace. 1. Follow the instructions in the [configuration](configuration.ipynb) notebook to create and connect to a workspace.
1. Open one of the sample notebooks. 1. Open one of the sample notebooks.
<a name="databricks"></a> <a name="databricks"></a>
@@ -89,7 +89,7 @@ bash automl_setup_linux.sh
``` ```
### 4. Running configuration.ipynb ### 4. Running configuration.ipynb
- Before running any samples you next need to run the configuration notebook. Click on 00.configuration.ipynb notebook - Before running any samples you next need to run the configuration notebook. Click on configuration.ipynb notebook
- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*) - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
### 5. Running Samples ### 5. Running Samples
@@ -98,22 +98,21 @@ bash automl_setup_linux.sh
<a name="samples"></a> <a name="samples"></a>
# Automated ML SDK Sample Notebooks # Automated ML SDK Sample Notebooks
- [00.configuration.ipynb](00.configuration.ipynb) - [configuration.ipynb](configuration.ipynb)
- Register Machine Learning Services Resource Provider
- Create new Azure ML Workspace - Create new Azure ML Workspace
- Save Workspace configuration file - Save Workspace configuration file
- [01.auto-ml-classification.ipynb](01.auto-ml-classification.ipynb) - [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Simple example of using Auto ML for classification - Simple example of using Auto ML for classification
- Uses local compute for training - Uses local compute for training
- [02.auto-ml-regression.ipynb](02.auto-ml-regression.ipynb) - [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
- Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
- Simple example of using Auto ML for regression - Simple example of using Auto ML for regression
- Uses local compute for training - Uses local compute for training
- [03.auto-ml-remote-execution.ipynb](03.auto-ml-remote-execution.ipynb) - [auto-ml-remote-execution.ipynb](remote-execution/auto-ml-remote-execution.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Example of using Auto ML for classification using a remote linux DSVM for training - Example of using Auto ML for classification using a remote linux DSVM for training
- Parallel execution of iterations - Parallel execution of iterations
@@ -122,7 +121,7 @@ bash automl_setup_linux.sh
- Retrieving models for any iteration or logged metric - Retrieving models for any iteration or logged metric
- Specify automl settings as kwargs - Specify automl settings as kwargs
- [03b.auto-ml-remote-batchai.ipynb](03b.auto-ml-remote-batchai.ipynb) - [auto-ml-remote-batchai.ipynb](remote-batchai/auto-ml-remote-batchai.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Example of using automated ML for classification using a remote Batch AI compute for training - Example of using automated ML for classification using a remote Batch AI compute for training
- Parallel execution of iterations - Parallel execution of iterations
@@ -131,80 +130,60 @@ bash automl_setup_linux.sh
- Retrieving models for any iteration or logged metric - Retrieving models for any iteration or logged metric
- Specify automl settings as kwargs - Specify automl settings as kwargs
- [04.auto-ml-remote-execution-text-data-blob-store.ipynb](04.auto-ml-remote-execution-text-data-blob-store.ipynb) - [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
- Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) - Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/)
- handling text data with preprocess flag - handling text data with preprocess flag
- Reading data from a blob store for remote executions - Reading data from a blob store for remote executions
- using pandas dataframes for reading data - using pandas dataframes for reading data
- [05.auto-ml-missing-data-blacklist-early-termination.ipynb](05.auto-ml-missing-data-blacklist-early-termination.ipynb) - [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Blacklist certain pipelines - Blacklist certain pipelines
- Specify a target metrics to indicate stopping criteria - Specify a target metrics to indicate stopping criteria
- Handling Missing Data in the input - Handling Missing Data in the input
- [06.auto-ml-sparse-data-train-test-split.ipynb](06.auto-ml-sparse-data-train-test-split.ipynb) - [auto-ml-sparse-data-train-test-split.ipynb](sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb)
- Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html) - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
- Handle sparse datasets - Handle sparse datasets
- Specify custom train and validation set - Specify custom train and validation set
- [07.auto-ml-exploring-previous-runs.ipynb](07.auto-ml-exploring-previous-runs) - [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
- List all projects for the workspace - List all projects for the workspace
- List all AutoML Runs for a given project - List all AutoML Runs for a given project
- Get details for a AutoML Run. (Automl settings, run widget & all metrics) - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
- Download fitted pipeline for any iteration - Download fitted pipeline for any iteration
- [08.auto-ml-remote-execution-with-DataStore.ipynb](08.auto-ml-remote-execution-with-DataStore.ipynb) - [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
- Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/) - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
- Download the data and store it in DataStore. - Download the data and store it in DataStore.
- [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb) - [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Simple example of using Auto ML for classification - Simple example of using Auto ML for classification
- Registering the model - Registering the model
- Creating Image and creating aci service - Creating Image and creating aci service
- Testing the aci service - Testing the aci service
- [10.auto-ml-multi-output-example.ipynb](10.auto-ml-multi-output-example.ipynb) - [auto-ml-sample-weight.ipynb](sample-weight/auto-ml-sample-weight.ipynb)
- Dataset: scikit learn's random example using multi-output pipeline(http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)
- Simple example of using Auto ML for multi output regression
- Handle both the dense and sparse metrix
- [11.auto-ml-sample-weight.ipynb](11.auto-ml-sample-weight.ipynb)
- How to specifying sample_weight - How to specifying sample_weight
- The difference that it makes to test results - The difference that it makes to test results
- [12.auto-ml-retrieve-the-training-sdk-versions.ipynb](12.auto-ml-retrieve-the-training-sdk-versions.ipynb) - [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
- How to get current and training env SDK versions
- [13.auto-ml-dataprep.ipynb](13.auto-ml-dataprep.ipynb)
- Using DataPrep for reading data - Using DataPrep for reading data
- [14.auto-ml-model-explanation.ipynb](14.auto-ml-model-explanation.ipynb) - [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
- Dataset: sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) - Using DataPrep for reading data with remote execution
- Explaining the AutoML classification pipeline
- Visualizing feature importance in widget
- [15a.auto-ml-classification-ensemble.ipynb](15a.auto-ml-classification-ensemble.ipynb) - [auto-ml-classification-local-azuredatabricks.ipynb](classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Enables an extra iteration for generating an Ensemble of models
- Uses local compute for training
- [15b.auto-ml-regression-ensemble.ipynb](15b.auto-ml-regression-ensemble.ipynb)
- Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
- Enables an extra iteration for generating an Ensemble of models
- Uses remote Linux DSVM for training
- [16a.auto-ml-classification-local-azuredatabricks.ipynb](16a.auto-ml-classification-local-azuredatabricks.ipynb)
- Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/) - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
- Example of using AutoML for classification using Azure Databricks as the platform for training - Example of using AutoML for classification using Azure Databricks as the platform for training
- [17.auto-ml-classification_with_tensorflow.ipynb](17.auto-ml-classification_with_tensorflow.ipynb) - [auto-ml-classification_with_tensorflow.ipynb](classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Simple example of using Auto ML for classification with whitelisting tensorflow models.checkout - Simple example of using Auto ML for classification with whitelisting tensorflow models.checkout
- Uses local compute for training - Uses local compute for training
- [18.auto-ml-timeseries.ipynb](18.auto-ml-timeseries.ipynb) - [auto-ml-timeseries.ipynb](timeseries/auto-ml-timeseries.ipynb)
- Dataset: NYC energy demanding data - Dataset: NYC energy demanding data
- Example of using AutoML for timeseries data training - Example of using AutoML for timeseries data training

501
how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb Normal file
View File

@@ -0,0 +1,501 @@
{
"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": [
"# Automated Machine Learning: Classification with Deployment\n",
"\n",
"In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an experiment using an existing workspace.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using local compute.\n",
"4. Explore the results.\n",
"5. Register the model.\n",
"6. Create a container image.\n",
"7. Create an Azure Container Instance (ACI) service.\n",
"8. Test the ACI service.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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 json\n",
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-local-classification'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-classification'\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": [
"## 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": [
"## Configure AutoML\n",
"\n",
"Instantiate a AutoMLConfig object. This defines 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>average_precision_score_weighted</i><br><i>norm_macro_recall</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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits = datasets.load_digits()\n",
"X_train = digits.data[10:,:]\n",
"y_train = digits.target[10:]\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" name = experiment_name,\n",
" debug_log = 'automl_errors.log',\n",
" primary_metric = 'AUC_weighted',\n",
" iteration_timeout_minutes = 20,\n",
" iterations = 10,\n",
" n_cross_validations = 2,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model\n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. 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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Register the Fitted Model for Deployment\n",
"If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"description = 'AutoML Model'\n",
"tags = None\n",
"model = local_run.register_model(description = description, tags = tags)\n",
"local_run.model_id # This will be written to the script file later in the notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Scoring Script"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"import azureml.train.automl\n",
"from sklearn.externals import joblib\n",
"from azureml.core.model import Model\n",
"\n",
"\n",
"def init():\n",
" global model\n",
" model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"def run(rawdata):\n",
" try:\n",
" data = json.loads(rawdata)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
" return json.dumps({\"error\": result})\n",
" return json.dumps({\"result\":result.tolist()})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a YAML File for the Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'automl-local-classification'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Substitute the actual version number in the environment file.\n",
"# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
"# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
"\n",
"with open(conda_env_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
"script_file_name = 'score.py'\n",
"\n",
"with open(script_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"with open(script_file_name, 'w') as cefw:\n",
" cefw.write(content.replace('<<modelid>>', local_run.model_id))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
" description = \"Image for automl classification sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
" description = 'sample service for Automl Classification')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'automl-sample-01'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete a Web Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Logs from a Deployed Web Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test a Web Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Randomly select digits and test\n",
"digits = datasets.load_digits()\n",
"X_test = digits.data[:10, :]\n",
"y_test = digits.target[:10]\n",
"images = digits.images[:10]\n",
"\n",
"for index in np.random.choice(len(y_test), 3, replace = False):\n",
" print(index)\n",
" test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
" predicted = aci_service.run(input_data = test_sample)\n",
" label = y_test[index]\n",
" predictedDict = json.loads(predicted)\n",
" title = \"Label value = %d Predicted value = %s \" % ( label,predictedDict['result'][0])\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()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

414
how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb Normal file
View File

@@ -0,0 +1,414 @@
{
"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": [
"# Automated Machine Learning: Classification with Local Compute\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",
"\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 using local compute.\n",
"4. Explore the results.\n",
"5. Test the best fitted model.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "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-classification'\n",
"project_folder = './sample_projects/automl-local-classification'\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": [
"## 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": [
"## Load Training 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": [
"from sklearn import datasets\n",
"\n",
"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": [
"## Configure AutoML\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
]
},
{
"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 = 25,\n",
" n_cross_validations = 3,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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": [
"Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = local_run.continue_experiment(X = X_train, \n",
" y = y_train, \n",
" show_output = True,\n",
" iterations = 5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the 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 the Best Fitted Model\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()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

390
how-to-use-azureml/automated-machine-learning/classification_with_tensorflow/auto-ml-classification_with_tensorflow.ipynb Normal file
View File

@@ -0,0 +1,390 @@
{
"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": [
"# Automated Machine Learning: Classification with Local Compute with Tesnorflow DNNClassifier and LinearClassifier using whitelist models\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.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "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-classification'\n",
"project_folder = './sample_projects/automl-local-classification'\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": [
"## 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": [
"## Load Training 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": [
"from sklearn import datasets\n",
"\n",
"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": [
"## Configure AutoML\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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
]
},
{
"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",
" n_cross_validations = 3,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" enable_tf=True,\n",
" whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the 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 the Best Fitted Model\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()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

497
how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb Normal file
View File

@@ -0,0 +1,497 @@
{
"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": [
"# Automated Machine Learning: Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)\n",
"In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
"2. Pass the `Dataflow` to AutoML for a local run.\n",
"3. Pass the `Dataflow` to AutoML for a remote run."
]
},
{
"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": [
"## Create an Experiment\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",
"import os\n",
"import time\n",
"\n",
"import pandas as pd\n",
"\n",
"import azureml.core\n",
"from azureml.core.compute import DsvmCompute\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"import azureml.dataprep as dprep\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 experiment\n",
"experiment_name = 'automl-dataprep-remote-dsvm'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-dataprep-remote-dsvm'\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": [
"## Loading Data using DataPrep"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
"# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
"simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
"X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1) # Remove the header row.\n",
"\n",
"# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
"# and convert column types manually.\n",
"# Here we read a comma delimited file and convert all columns to integers.\n",
"y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review the Data Preparation Result\n",
"\n",
"You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.skip(1).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\n",
"\n",
"This creates a general AutoML settings object applicable for both local and remote runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\" : 10,\n",
" \"iterations\" : 2,\n",
" \"primary_metric\" : 'AUC_weighted',\n",
" \"preprocess\" : False,\n",
" \"verbosity\" : logging.INFO,\n",
" \"n_cross_validations\": 3\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Remote Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create or Attach a Remote Linux DSVM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dsvm_name = 'mydsvmc'\n",
"\n",
"try:\n",
" while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
" time.sleep(1)\n",
" \n",
" dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
" print('Found existing DVSM.')\n",
"except:\n",
" print('Creating a new DSVM.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
" dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
" dsvm_compute.wait_for_completion(show_output = True)\n",
" print(\"Waiting one minute for ssh to be accessible\")\n",
" time.sleep(60) # Wait for ssh to be accessible"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"conda_run_config.target = dsvm_compute\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pass Data with `Dataflow` Objects\n",
"\n",
"The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" X = X,\n",
" y = y,\n",
" **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the 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(remote_run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 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(remote_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",
"import pandas as pd\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. 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 = remote_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 = remote_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 first iteration:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 0\n",
"best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model\n",
"\n",
"#### Load Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"\n",
"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",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import imshow\n",
"import random\n",
"import numpy as np\n",
"\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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Appendix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Capture the `Dataflow` Objects for Later Use in AutoML\n",
"\n",
"`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# sklearn.digits.data + target\n",
"digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits_complete.to_pandas_dataframe().shape\n",
"labels_column = 'Column64'\n",
"dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
"dflow_y = digits_complete.keep_columns(columns = [labels_column])"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

446
how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb Normal file
View File

@@ -0,0 +1,446 @@
{
"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": [
"# Automated Machine Learning: Prepare Data using `azureml.dataprep` for Local Execution\n",
"In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
"2. Pass the `Dataflow` to AutoML for a local run.\n",
"3. Pass the `Dataflow` to AutoML for a remote run."
]
},
{
"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": [
"## Create an Experiment\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",
"import os\n",
"\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",
"import azureml.dataprep as dprep\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 experiment\n",
"experiment_name = 'automl-dataprep-local'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-dataprep-local'\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": [
"## Loading Data using DataPrep"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
"# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
"simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
"X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1) # Remove the header row.\n",
"\n",
"# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
"# and convert column types manually.\n",
"# Here we read a comma delimited file and convert all columns to integers.\n",
"y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review the Data Preparation Result\n",
"\n",
"You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X.skip(1).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\n",
"\n",
"This creates a general AutoML settings object applicable for both local and remote runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\" : 10,\n",
" \"iterations\" : 2,\n",
" \"primary_metric\" : 'AUC_weighted',\n",
" \"preprocess\" : False,\n",
" \"verbosity\" : logging.INFO,\n",
" \"n_cross_validations\": 3\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Local Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pass Data with `Dataflow` Objects\n",
"\n",
"The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" X = X,\n",
" y = y,\n",
" **automl_settings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the 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": [
"#### 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",
"import pandas as pd\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. 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 first iteration:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 0\n",
"best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
"print(best_run)\n",
"print(fitted_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model\n",
"\n",
"#### Load Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"\n",
"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",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import imshow\n",
"import random\n",
"import numpy as np\n",
"\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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Appendix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Capture the `Dataflow` Objects for Later Use in AutoML\n",
"\n",
"`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# sklearn.digits.data + target\n",
"digits_complete = dprep.auto_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`digits_complete` (sourced from `sklearn.datasets.load_digits()`) is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits_complete.to_pandas_dataframe().shape\n",
"labels_column = 'Column64'\n",
"dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
"dflow_y = digits_complete.keep_columns(columns = [labels_column])"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

336
how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb Normal file
View File

@@ -0,0 +1,336 @@
{
"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": [
"# Automated Machine Learning: Exploring Previous Runs\n",
"\n",
"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",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. List all experiments in a workspace.\n",
"2. List all AutoML runs in an experiment.\n",
"3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
"4. Download a fitted pipeline for any iteration.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# List all AutoML Experiments in a Workspace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import random\n",
"import re\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.run import Run\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,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"experiment_list = Experiment.list(workspace=ws)\n",
"\n",
"summary_df = pd.DataFrame(index = ['No of Runs'])\n",
"pattern = re.compile('^AutoML_[^_]*$')\n",
"for experiment in experiment_list:\n",
" all_runs = list(experiment.get_runs())\n",
" automl_runs = []\n",
" for run in all_runs:\n",
" if(pattern.match(run.id)):\n",
" automl_runs.append(run) \n",
" summary_df[experiment.name] = [len(automl_runs)]\n",
" \n",
"pd.set_option('display.max_colwidth', -1)\n",
"summary_df.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagnostics\n",
"\n",
"Opt-in diagnostics for better experience, quality, and security of future releases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.telemetry import set_diagnostics_collection\n",
"set_diagnostics_collection(send_diagnostics = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# List AutoML runs for an experiment\n",
"Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
"\n",
"proj = ws.experiments[experiment_name]\n",
"summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
"pattern = re.compile('^AutoML_[^_]*$')\n",
"all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
"automl_runs_project = []\n",
"for run in all_runs:\n",
" if(pattern.match(run.id)):\n",
" properties = run.get_properties()\n",
" tags = run.get_tags()\n",
" amlsettings = eval(properties['RawAMLSettingsString'])\n",
" if 'iterations' in tags:\n",
" iterations = tags['iterations']\n",
" else:\n",
" iterations = properties['num_iterations']\n",
" summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
" if run.get_details()['status'] == 'Completed':\n",
" automl_runs_project.append(run.id)\n",
" \n",
"from IPython.display import HTML\n",
"projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
"\n",
"from IPython.display import display\n",
"display(projname_html)\n",
"display(summary_df.T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get details for an AutoML run\n",
"\n",
"Copy the project name and run id from the previous cell output to find more details on a particular run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run_id = automl_runs_project[0] # Replace with your own run_id from above run ids\n",
"assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
"\n",
"from azureml.widgets import RunDetails\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
"\n",
"summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
"properties = ml_run.get_properties()\n",
"tags = ml_run.get_tags()\n",
"status = ml_run.get_details()\n",
"amlsettings = eval(properties['RawAMLSettingsString'])\n",
"if 'iterations' in tags:\n",
" iterations = tags['iterations']\n",
"else:\n",
" iterations = properties['num_iterations']\n",
"start_time = None\n",
"if 'startTimeUtc' in status:\n",
" start_time = status['startTimeUtc']\n",
"end_time = None\n",
"if 'endTimeUtc' in status:\n",
" end_time = status['endTimeUtc']\n",
"summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
"display(HTML('<h3>Runtime Details</h3>'))\n",
"display(summary_df)\n",
"\n",
"#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
"display(HTML('<h3>AutoML Settings</h3>'))\n",
"display(amlsettings)\n",
"\n",
"display(HTML('<h3>Iterations</h3>'))\n",
"RunDetails(ml_run).show() \n",
"\n",
"children = list(ml_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",
"display(HTML('<h3>Metrics</h3>'))\n",
"display(rundata)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Download fitted models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download the Best Model for Any Given Metric"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metric = 'AUC_weighted' # Replace with a metric name.\n",
"best_run, fitted_model = ml_run.get_output(metric = metric)\n",
"fitted_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download the Model for Any Given Iteration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 1 # Replace with an iteration number.\n",
"best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
"fitted_model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Register fitted model for deployment\n",
"If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"description = 'AutoML Model'\n",
"tags = None\n",
"ml_run.register_model(description = description, tags = tags)\n",
"ml_run.model_id # Use this id to deploy the model as a web service in Azure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register the Best Model for Any Given Metric"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metric = 'AUC_weighted' # Replace with a metric name.\n",
"description = 'AutoML Model'\n",
"tags = None\n",
"ml_run.register_model(description = description, tags = tags, metric = metric)\n",
"print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register the Model for Any Given Iteration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 1 # Replace with an iteration number.\n",
"description = 'AutoML Model'\n",
"tags = None\n",
"ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
"print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

398
how-to-use-azureml/automated-machine-learning/forecasting-a/auto-ml-forecasting-a.ipynb Normal file
View File

@@ -0,0 +1,398 @@
{
"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": [
"# Automated Machine Learning: Energy Demand Forecasting\n",
"\n",
"In this example, we show how AutoML can be used for energy demand forecasting.\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you would see\n",
"1. Creating an Experiment in an existing Workspace\n",
"2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
"3. Training the Model using local compute\n",
"4. Exploring the results\n",
"5. Testing the fitted model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import logging\n",
"import warnings\n",
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.pyplot import imshow\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-energydemandforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-energydemandforecasting'\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['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.DataFrame(data=output, index=['']).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Data\n",
"Read energy demanding data from file, and preview data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Split the data to train and test\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train = data[data['timeStamp'] < '2017-02-01']\n",
"test = data[data['timeStamp'] >= '2017-02-01']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare the test data, we will feed X_test to the fitted model and get prediction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test = test.pop('demand').values\n",
"X_test = test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Split the train data to train and valid\n",
"\n",
"Use one month's data as valid data\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train = train[train['timeStamp'] < '2017-01-01']\n",
"X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
"y_train = X_train.pop('demand').values\n",
"y_valid = X_valid.pop('demand').values\n",
"print(X_train.shape)\n",
"print(y_train.shape)\n",
"print(X_valid.shape)\n",
"print(y_valid.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiate Auto ML Config\n",
"\n",
"Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|forecasting|\n",
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
"|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers. |\n",
"|**X_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y_valid**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers. |\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. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"time_column_name = 'timeStamp'\n",
"automl_settings = {\n",
" \"time_column_name\": time_column_name,\n",
"}\n",
"\n",
"\n",
"automl_config = AutoMLConfig(task = 'forecasting',\n",
" debug_log = 'automl_nyc_energy_errors.log',\n",
" primary_metric='normalized_root_mean_squared_error',\n",
" iterations = 10,\n",
" iteration_timeout_minutes = 5,\n",
" X = X_train,\n",
" y = y_train,\n",
" X_valid = X_valid,\n",
" y_valid = y_valid,\n",
" path=project_folder,\n",
" verbosity = logging.INFO,\n",
" **automl_settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the Model\n",
"\n",
"You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
"You will see the currently running iterations printing to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model\n",
"Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = local_run.get_output()\n",
"fitted_model.steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model\n",
"\n",
"Predict on training and test set, and calculate residual values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = fitted_model.predict(X_test)\n",
"y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define a Check Data Function\n",
"\n",
"Remove the nan values from y_test to avoid error when calculate metrics "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def _check_calc_input(y_true, y_pred, rm_na=True):\n",
" \"\"\"\n",
" Check that 'y_true' and 'y_pred' are non-empty and\n",
" have equal length.\n",
"\n",
" :param y_true: Vector of actual values\n",
" :type y_true: array-like\n",
"\n",
" :param y_pred: Vector of predicted values\n",
" :type y_pred: array-like\n",
"\n",
" :param rm_na:\n",
" If rm_na=True, remove entries where y_true=NA and y_pred=NA.\n",
" :type rm_na: boolean\n",
"\n",
" :return:\n",
" Tuple (y_true, y_pred). if rm_na=True,\n",
" the returned vectors may differ from their input values.\n",
" :rtype: Tuple with 2 entries\n",
" \"\"\"\n",
" if len(y_true) != len(y_pred):\n",
" raise ValueError(\n",
" 'the true values and prediction values do not have equal length.')\n",
" elif len(y_true) == 0:\n",
" raise ValueError(\n",
" 'y_true and y_pred are empty.')\n",
" # if there is any non-numeric element in the y_true or y_pred,\n",
" # the ValueError exception will be thrown.\n",
" y_true = np.array(y_true).astype(float)\n",
" y_pred = np.array(y_pred).astype(float)\n",
" if rm_na:\n",
" # remove entries both in y_true and y_pred where at least\n",
" # one element in y_true or y_pred is missing\n",
" y_true_rm_na = y_true[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
" y_pred_rm_na = y_pred[~(np.isnan(y_true) | np.isnan(y_pred))]\n",
" return (y_true_rm_na, y_pred_rm_na)\n",
" else:\n",
" return y_true, y_pred"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test,y_pred = _check_calc_input(y_test,y_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate metrics for the prediction\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
"# Explained variance score: 1 is perfect prediction\n",
"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
"print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
"\n",
"\n",
"\n",
"# Plot outputs\n",
"%matplotlib notebook\n",
"test_pred = plt.scatter(y_test, y_pred, color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
"plt.show()"
]
}
],
"metadata": {
"authors": [
{
"name": "xiaga"
}
],
"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
}

49206
how-to-use-azureml/automated-machine-learning/forecasting-a/nyc_energy.csv Normal file
View File

File diff suppressed because it is too large Load Diff

394
how-to-use-azureml/automated-machine-learning/forecasting-b/auto-ml-forecasting-b.ipynb Normal file
View File

@@ -0,0 +1,394 @@
{
"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": [
"# Automated Machine Learning: Orange Juice Sales Forecasting\n",
"\n",
"In this example, we use AutoML to find and tune a time-series forecasting model.\n",
"\n",
"Make sure you have executed the [configuration notebook](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook, you will:\n",
"1. Create an Experiment in an existing Workspace\n",
"2. Instantiate an AutoMLConfig \n",
"3. Find and train a forecasting model using local compute\n",
"4. Evaluate the performance of the model\n",
"\n",
"## Sample Data\n",
"The examples in the follow code samples use the [University of Chicago's Dominick's Finer Foods dataset](https://research.chicagobooth.edu/kilts/marketing-databases/dominicks) to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import logging\n",
"import warnings\n",
"# Squash warning messages for cleaner output in the notebook\n",
"warnings.showwarning = lambda *args, **kwargs: None\n",
"\n",
"\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun\n",
"from sklearn.metrics import mean_absolute_error, mean_squared_error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-ojsalesforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-ojsalesforecasting'\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['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.DataFrame(data=output, index=['']).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Data\n",
"You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"time_column_name = 'WeekStarting'\n",
"data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred. \n",
"\n",
"The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"grain_column_names = ['Store', 'Brand']\n",
"nseries = data.groupby(grain_column_names).ngroups\n",
"print('Data contains {0} individual time-series.'.format(nseries))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Splitting\n",
"For the purposes of demonstration and later forecast evaluation, we now split the data into a training and a testing set. The test set will contain the final 20 weeks of observed sales for each time-series."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ntest_periods = 20\n",
"\n",
"def split_last_n_by_grain(df, n):\n",
" \"\"\"\n",
" Group df by grain and split on last n rows for each group\n",
" \"\"\"\n",
" df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
" .groupby(grain_column_names, group_keys=False))\n",
" df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
" df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
" return df_head, df_tail\n",
"\n",
"X_train, X_test = split_last_n_by_grain(data, ntest_periods)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modeling\n",
"\n",
"For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
"* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
"* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
"* Create grain-based features to enable fixed effects across different series\n",
"* Create time-based features to assist in learning seasonal patterns\n",
"* Encode categorical variables to numeric quantities\n",
"\n",
"AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
"\n",
"You are almost ready to start an AutoML training job. We will first need to create a validation set from the existing training set (i.e. for hyper-parameter tuning): "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nvalidation_periods = 20\n",
"X_train, X_validate = split_last_n_by_grain(X_train, nvalidation_periods)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also need to separate the target column from the rest of the DataFrame: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_column_name = 'Quantity'\n",
"y_train = X_train.pop(target_column_name).values\n",
"y_validate = X_validate.pop(target_column_name).values "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an AutoMLConfig\n",
"\n",
"The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, and the training and validation data. \n",
"\n",
"For forecasting tasks, there are some additional parameters that can be set: the name of the input data column, holding the date/time and the grain column names. A time column is required for forecasting, while the grain is optional. If a grain is not given, the forecaster assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak. \n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|forecasting|\n",
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
"|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
"|**X**|Training matrix of features, shape = [n_training_samples, n_features]|\n",
"|**y**|Target values, shape = [n_training_samples, ]|\n",
"|**X_valid**|Validation matrix of features, shape = [n_validation_samples, n_features]|\n",
"|**y_valid**|Target values for validation, shape = [n_validation_samples, ]\n",
"|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
"|**debug_log**|Log file path for writing debugging information\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. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" 'time_column_name': time_column_name,\n",
" 'grain_column_names': grain_column_names,\n",
" 'drop_column_names': ['logQuantity']\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task='forecasting',\n",
" debug_log='automl_oj_sales_errors.log',\n",
" primary_metric='normalized_root_mean_squared_error',\n",
" iterations=10,\n",
" X=X_train,\n",
" y=y_train,\n",
" X_valid=X_validate,\n",
" y_valid=y_validate,\n",
" enable_ensembling=False,\n",
" path=project_folder,\n",
" verbosity=logging.INFO,\n",
" **automl_settings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the Model\n",
"\n",
"You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
"Information from each iteration will be printed to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best Model\n",
"Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_pipeline = local_run.get_output()\n",
"fitted_pipeline.steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make Predictions from the Best Fitted Model\n",
"Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_test = X_test.pop(target_column_name).values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_test.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
"\n",
"The target predictions can be retrieved by calling the `predict` method on the best model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred = fitted_pipeline.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculate evaluation metrics for the prediction\n",
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def MAPE(actual, pred):\n",
" \"\"\"\n",
" Calculate mean absolute percentage error.\n",
" Remove NA and values where actual is close to zero\n",
" \"\"\"\n",
" not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
" not_zero = ~np.isclose(actual, 0.0)\n",
" actual_safe = actual[not_na & not_zero]\n",
" pred_safe = pred[not_na & not_zero]\n",
" APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
" return np.mean(APE)\n",
"\n",
"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
"print('MAPE: %.2f' % MAPE(y_test, y_pred))"
]
}
],
"metadata": {
"authors": [
{
"name": "erwright"
}
],
"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
}

28948
how-to-use-azureml/automated-machine-learning/forecasting-b/dominicks_OJ.csv Normal file
View File

File diff suppressed because it is too large Load Diff

381
how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb Normal file
View File

@@ -0,0 +1,381 @@
{
"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": [
"# Automated Machine Learning: Blacklisting Models, Early Termination, and Handling Missing Data\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 handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\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",
"4. Train the model.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"In addition this notebook showcases the following features\n",
"- **Blacklisting** certain pipelines\n",
"- Specifying **target metrics** to indicate stopping criteria\n",
"- Handling **missing data** in the input\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-local-missing-data'\n",
"project_folder = './sample_projects/automl-local-missing-data'\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": [
"## 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": [
"### Creating missing data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scipy import sparse\n",
"\n",
"digits = datasets.load_digits()\n",
"X_train = digits.data[10:,:]\n",
"y_train = digits.target[10:]\n",
"\n",
"# Add missing values in 75% of the lines.\n",
"missing_rate = 0.75\n",
"n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
"missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
"rng = np.random.RandomState(0)\n",
"rng.shuffle(missing_samples)\n",
"missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
"X_train[np.where(missing_samples)[0], missing_features] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(data = X_train)\n",
"df['Label'] = pd.Series(y_train, index=df.index)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment. This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
"|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
"|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
"|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
]
},
{
"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 = 20,\n",
" n_cross_validations = 5,\n",
" preprocess = True,\n",
" experiment_exit_score = 0.9984,\n",
" blacklist_models = ['KNN','LinearSVM'],\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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": "markdown",
"metadata": {},
"source": [
"## Explore the 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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Best Model Based on Any Other Metric\n",
"Show the run and the model which has the smallest `accuracy` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# lookup_metric = \"accuracy\"\n",
"# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
]
},
{
"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",
"# best_run, fitted_model = local_run.get_output(iteration = iteration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing the best Fitted Model"
]
},
{
"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]\n",
"\n",
"# 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()\n"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

348
how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb Normal file
View File

@@ -0,0 +1,348 @@
{
"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": [
"# Automated Machine Learning: Explain classification model and visualize the explanation\n",
"\n",
"In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you would see\n",
"1. Creating an Experiment in an existing Workspace\n",
"2. Instantiating AutoMLConfig\n",
"3. Training the Model using local compute and explain the model\n",
"4. Visualization model's feature importance in widget\n",
"5. Explore best model's explanation\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import random\n",
"\n",
"import pandas as pd\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,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-local-classification'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-classification-model-explanation'\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": [
"## 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": [
"## Load Iris Data Set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"\n",
"iris = datasets.load_iris()\n",
"y = iris.target\n",
"X = iris.data\n",
"\n",
"features = iris.feature_names\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,\n",
" y,\n",
" test_size=0.1,\n",
" random_state=100,\n",
" stratify=y)\n",
"\n",
"X_train = pd.DataFrame(X_train, columns=features)\n",
"X_test = pd.DataFrame(X_test, columns=features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiate Auto ML Config\n",
"\n",
"Instantiate a AutoMLConfig object. This defines 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>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
"|**max_time_sec**|Time limit in minutes for each iterations|\n",
"|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers. |\n",
"|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
"|**model_explainability**|Indicate to explain each trained pipeline or not |\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. |"
]
},
{
"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 = 200,\n",
" iterations = 10,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" X_valid = X_test,\n",
" y_valid = y_test,\n",
" model_explainability=True,\n",
" path=project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the Model\n",
"\n",
"You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
"You will see the currently running iterations printing to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_run = experiment.submit(automl_config, show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Widget for monitoring runs\n",
"\n",
"The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed 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. This links to a web-ui 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": [
"### Retrieve the Best Model\n",
"\n",
"Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or 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 's explanation\n",
"\n",
"Retrieve the explanation from the best_run. And explanation information includes:\n",
"\n",
"1.\tshap_values: The explanation information generated by shap lib\n",
"2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
"3.\toverall_summary: The model level feature importance values sorted in descending order\n",
"4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
"5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
"6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
"\n",
"shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
" retrieve_model_explanation(best_run)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(overall_summary)\n",
"print(overall_imp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(per_class_summary)\n",
"print(per_class_imp)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Beside retrieve the existed model explanation information, explain the model with different train/test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.automlexplainer import explain_model\n",
"\n",
"shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
" explain_model(fitted_model, X_train, X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(overall_summary)\n",
"print(overall_imp)"
]
}
],
"metadata": {
"authors": [
{
"name": "xif"
}
],
"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
}

415
how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb Normal file
View File

@@ -0,0 +1,415 @@
{
"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: Regression with Local Compute\n",
"\n",
"In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression problem.\n",
"\n",
"Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\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 using local compute.\n",
"4. Explore the results.\n",
"5. Test the best fitted model.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "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-regression'\n",
"project_folder = './sample_projects/automl-local-regression'\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": [
"## 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": [
"### Load Training Data\n",
"This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\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. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</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, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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.|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_config = AutoMLConfig(task = 'regression',\n",
" iteration_timeout_minutes = 10,\n",
" iterations = 10,\n",
" primary_metric = 'spearman_correlation',\n",
" n_cross_validations = 5,\n",
" debug_log = 'automl.log',\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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": [
"## Explore the 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 `root_mean_squared_error` value (which turned out to be the same as the one with largest `spearman_correlation` value):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"root_mean_squared_error\"\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 the Best Fitted Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predict on training and test set, and calculate residual values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y_pred_train = fitted_model.predict(X_train)\n",
"y_residual_train = y_train - y_pred_train\n",
"\n",
"y_pred_test = fitted_model.predict(X_test)\n",
"y_residual_test = y_test - y_pred_test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn import datasets\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"# Set up a multi-plot chart.\n",
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
"f.suptitle('Regression Residual Values', fontsize = 18)\n",
"f.set_figheight(6)\n",
"f.set_figwidth(16)\n",
"\n",
"# Plot residual values of training set.\n",
"a0.axis([0, 360, -200, 200])\n",
"a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
"a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
"a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
"a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
"a0.set_xlabel('Training samples', fontsize = 12)\n",
"a0.set_ylabel('Residual Values', fontsize = 12)\n",
"\n",
"# Plot a histogram.\n",
"a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
"a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
"\n",
"# Plot residual values of test set.\n",
"a1.axis([0, 90, -200, 200])\n",
"a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
"a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
"a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
"a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
"a1.set_xlabel('Test samples', fontsize = 12)\n",
"a1.set_yticklabels([])\n",
"\n",
"# Plot a histogram.\n",
"a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step')\n",
"a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10)\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

517
how-to-use-azureml/automated-machine-learning/remote-attach/auto-ml-remote-attach.ipynb Normal file
View File

@@ -0,0 +1,517 @@
{
"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": [
"# Automated Machine Learning: Remote Execution using attach\n",
"\n",
"In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML to handle text data with remote attach.\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Attach an existing DSVM to a workspace.\n",
"3. Configure AutoML using `AutoMLConfig`.\n",
"4. Train the model using the DSVM.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"In addition this notebook showcases the following features\n",
"- **Parallel** executions for iterations\n",
"- **Asynchronous** tracking of progress\n",
"- **Cancellation** of individual iterations or the entire run\n",
"- Retrieving models for any iteration or logged metric\n",
"- Specifying AutoML settings as `**kwargs`\n",
"- Handling **text** data using the `preprocess` flag\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the run history container in the workspace.\n",
"experiment_name = 'automl-remote-dsvm-blobstore'\n",
"project_folder = './sample_projects/automl-remote-dsvm-blobstore'\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": [
"## 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": [
"## Attach a Remote Linux DSVM\n",
"To use a remote Docker compute target:\n",
"1. Create a Linux DSVM in Azure, following these [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor (not CentOS). Make sure that disk space is available under `/tmp` because AutoML creates files under `/tmp/azureml_run`s. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4GB per core.\n",
"2. Enter the IP address, user name and password below.\n",
"\n",
"**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on changing SSH ports for security reasons."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, RemoteCompute\n",
"import time\n",
"\n",
"# Add your VM information below\n",
"# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
"# dsvm_username and dsvm_password will be ignored.\n",
"compute_name = 'mydsvmb'\n",
"dsvm_ip_addr = '<<ip_addr>>'\n",
"dsvm_ssh_port = 22\n",
"dsvm_username = '<<username>>'\n",
"dsvm_password = '<<password>>'\n",
"\n",
"if compute_name in ws.compute_targets:\n",
" print('Using existing compute.')\n",
" dsvm_compute = ws.compute_targets[compute_name]\n",
"else:\n",
" attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
" ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
"\n",
" while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
" time.sleep(1)\n",
"\n",
" dsvm_compute = ws.compute_targets[compute_name]\n",
" \n",
" if dsvm_compute.provisioning_state == 'Failed':\n",
" print('Attached failed.')\n",
" print(dsvm_compute.provisioning_errors)\n",
" dsvm_compute.detach()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"conda_run_config.target = dsvm_compute\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Get Data File\n",
"For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
"In this example, the `get_data()` function returns a [dictionary](README.md#getdata)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"import numpy as np\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"\n",
"def get_data():\n",
" remove = ('headers', 'footers', 'quotes')\n",
" categories = [\n",
" 'alt.atheism',\n",
" 'talk.religion.misc',\n",
" 'comp.graphics',\n",
" 'sci.space',\n",
" ]\n",
" data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
" shuffle = True, random_state = 42,\n",
" remove = remove)\n",
" \n",
" X_train = np.array(data_train.data).reshape((len(data_train.data),1))\n",
" y_train = np.array(data_train.target)\n",
" \n",
" return { \"X\" : X_train, \"y\" : y_train }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
"\n",
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
"\n",
"**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
"\n",
"|Property|Description|\n",
"|-|-|\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
"|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
"|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\n",
"|**max_cores_per_iteration**|Indicates how many cores on the compute target would be used to train a single pipeline.<br>Default is *1*; you can set it to *-1* to use all cores.|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 60,\n",
" \"iterations\": 4,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": True,\n",
" \"max_cores_per_iteration\": 2\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
"\n",
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
"#### 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",
"You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\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(remote_run).show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Wait until the run finishes.\n",
"remote_run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-process cache cleanup\n",
"The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run.clean_preprocessor_cache()"
]
},
{
"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(remote_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": [
"## Cancelling Runs\n",
"You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cancel the ongoing experiment and stop scheduling new iterations.\n",
"# remote_run.cancel()\n",
"\n",
"# Cancel iteration 1 and move onto iteration 2.\n",
"# remote_run.cancel_iteration(1)"
]
},
{
"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. 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 = remote_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 which has the smallest `accuracy` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# lookup_metric = \"accuracy\"\n",
"# best_run, fitted_model = remote_run.get_output(metric = lookup_metric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model from a Specific Iteration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iteration = 0\n",
"zero_run, zero_model = remote_run.get_output(iteration = iteration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load test data.\n",
"from pandas_ml import ConfusionMatrix\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"\n",
"remove = ('headers', 'footers', 'quotes')\n",
"categories = [\n",
" 'alt.atheism',\n",
" 'talk.religion.misc',\n",
" 'comp.graphics',\n",
" 'sci.space',\n",
" ]\n",
"\n",
"data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
" shuffle = True, random_state = 42,\n",
" remove = remove)\n",
"\n",
"X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
"y_test = data_test.target\n",
"\n",
"# Test our best pipeline.\n",
"\n",
"y_pred = fitted_model.predict(X_test)\n",
"y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
"y_test_strings = [data_test.target_names[i] for i in y_test]\n",
"\n",
"cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
"print(cm)\n",
"cm.plot()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

528
how-to-use-azureml/automated-machine-learning/remote-batchai/auto-ml-remote-batchai.ipynb Normal file
View File

@@ -0,0 +1,528 @@
{
"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": [
"# Automated Machine Learning: Remote Execution using Batch AI\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",
"\n",
"In this notebook you would see\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Attach an existing Batch AI compute to a workspace.\n",
"3. Configure AutoML using `AutoMLConfig`.\n",
"4. Train the model using Batch AI.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"In addition this notebook showcases the following features\n",
"- **Parallel** executions for iterations\n",
"- **Asynchronous** tracking of progress\n",
"- **Cancellation** of individual iterations or the entire run\n",
"- Retrieving models for any iteration or logged metric\n",
"- Specifying AutoML settings as `**kwargs`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the run history container in the workspace.\n",
"experiment_name = 'automl-remote-batchai'\n",
"project_folder = './sample_projects/automl-remote-batchai'\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": [
"## 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": [
"## Create Batch AI Cluster\n",
"The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
"\n",
"**Note:** The creation of the Batch AI cluster can take over 10 minutes, please be patient.\n",
"\n",
"As with other Azure services, there are limits on certain resources (e.g. Batch AI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"batchai_cluster_name = \"automlcl\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
"cts = ws.compute_targets\n",
"if batchai_cluster_name in cts and cts[batchai_cluster_name].type == 'BatchAI':\n",
" found = True\n",
" print('Found existing compute target.')\n",
" compute_target = cts[batchai_cluster_name]\n",
" \n",
"if not found:\n",
" print('Creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
" #vm_priority = 'lowpriority', # optional\n",
" max_nodes = 6)\n",
"\n",
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, batchai_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current Batch AI cluster status, use the 'status' property."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Batch AI cluster\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Get Data File\n",
"For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
"In this example, the `get_data()` function returns data using 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": [
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"from sklearn import datasets\n",
"from scipy import sparse\n",
"import numpy as np\n",
"\n",
"def get_data():\n",
" \n",
" digits = datasets.load_digits()\n",
" X_train = digits.data\n",
" y_train = digits.target\n",
"\n",
" return { \"X\" : X_train, \"y\" : y_train }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
"\n",
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
"\n",
"**Note:** When using Batch AI, you can't pass Numpy arrays directly to the fit method.\n",
"\n",
"|Property|Description|\n",
"|-|-|\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 2,\n",
" \"iterations\": 20,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": False,\n",
" \"max_concurrent_iterations\": 5,\n",
" \"verbosity\": logging.INFO\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Results\n",
"\n",
"#### Loading executed runs\n",
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
]
},
{
"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",
"You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\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": [
"remote_run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Wait until the run finishes.\n",
"remote_run.wait_for_completion(show_output = True)"
]
},
{
"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(remote_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": [
"## Cancelling Runs\n",
"\n",
"You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cancel the ongoing experiment and stop scheduling new iterations.\n",
"# remote_run.cancel()\n",
"\n",
"# Cancel iteration 1 and move onto iteration 2.\n",
"# remote_run.cancel_iteration(1)"
]
},
{
"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 = remote_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 which has the smallest `log_loss` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"log_loss\"\n",
"best_run, fitted_model = remote_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 = remote_run.get_output(iteration=iteration)\n",
"print(third_run)\n",
"print(third_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\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"
]
},
{
"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()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

583
how-to-use-azureml/automated-machine-learning/remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb Normal file
View File

@@ -0,0 +1,583 @@
{
"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": [
"# Automated Machine Learning: Remote Execution with DataStore\n",
"\n",
"This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
"1. DataStore secures the access details.\n",
"2. DataStore supports read, write to blob and file store\n",
"3. AutoML natively supports copying data from DataStore to DSVM\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you would see\n",
"1. Storing data in DataStore.\n",
"2. get_data returning data from DataStore.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Experiment\n",
"\n",
"As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"import random\n",
"import time\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.compute import DsvmCompute\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,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-remote-datastore-file'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-remote-dsvm-file'\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": [
"## 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": [
"## Create a Remote Linux DSVM\n",
"Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
"\n",
"**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"compute_target_name = 'mydsvmc'\n",
"\n",
"try:\n",
" while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
" time.sleep(1)\n",
" \n",
" dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
" print('found existing:', dsvm_compute.name)\n",
"except:\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
" dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
" dsvm_compute.wait_for_completion(show_output=True)\n",
" print(\"Waiting one minute for ssh to be accessible\")\n",
" time.sleep(60) # Wait for ssh to be accessible"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Copy data file to local\n",
"\n",
"Download the data file.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mkdir data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_20newsgroups\n",
"import csv\n",
"\n",
"remove = ('headers', 'footers', 'quotes')\n",
"categories = [\n",
" 'alt.atheism',\n",
" 'talk.religion.misc',\n",
" 'comp.graphics',\n",
" 'sci.space',\n",
" ]\n",
"data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
" shuffle = True, random_state = 42,\n",
" remove = remove)\n",
" \n",
"pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
"pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload data to the cloud"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
"\n",
"The data.tsv files are uploaded into a directory named data at the root of the datastore."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace, Datastore\n",
"#blob_datastore = Datastore(ws, blob_datastore_name)\n",
"ds = ws.get_default_datastore()\n",
"print(ds.datastore_type, ds.account_name, ds.container_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ds.upload_files(\"data.tsv\")\n",
"ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run\n",
"\n",
"First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
"The path_on_compute should be an absolute path to ensure that the data files are downloaded only once. The get_data method should use this same path to access the data files."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import DataReferenceConfiguration\n",
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
" path_on_datastore='data', \n",
" path_on_compute='/tmp/azureml_runs',\n",
" mode='download', # download files from datastore to compute target\n",
" overwrite=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"conda_run_config.target = dsvm_compute\n",
"# set the data reference of the run coonfiguration\n",
"conda_run_config.data_references = {ds.name: dr}\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Get Data File\n",
"For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
"\n",
"The *get_data()* function returns a [dictionary](README.md#getdata).\n",
"\n",
"The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"import pandas as pd\n",
"\n",
"def get_data():\n",
" X_train = pd.read_csv(\"/tmp/azureml_runs/data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
" y_train = pd.read_csv(\"/tmp/azureml_runs/data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
"\n",
" return { \"X\" : X_train.values, \"y\" : y_train[0].values }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
"\n",
"You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
"\n",
"<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to AutoMLConfig.</i>\n",
"\n",
"|Property|Description|\n",
"|-|-|\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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 Auto ML trains a specific pipeline with the data|\n",
"|**n_cross_validations**|Number of cross validation splits|\n",
"|**max_concurrent_iterations**|Max number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM\n",
"|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
"|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\n",
"|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 60,\n",
" \"iterations\": 4,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": True,\n",
" \"max_cores_per_iteration\": 1,\n",
" \"verbosity\": logging.INFO\n",
"}\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path=project_folder,\n",
" run_configuration=conda_run_config,\n",
" #compute_target = dsvm_compute,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the Models <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
"\n",
"For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
"#### Widget for monitoring runs\n",
"\n",
"The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
"\n",
"You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
"\n",
"NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Wait until the run finishes.\n",
"remote_run.wait_for_completion(show_output = True)"
]
},
{
"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(remote_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": [
"## Canceling Runs\n",
"You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cancel the ongoing experiment and stop scheduling new iterations\n",
"# remote_run.cancel()\n",
"\n",
"# Cancel iteration 1 and move onto iteration 2\n",
"# remote_run.cancel_iteration(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-process cache cleanup\n",
"The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run.clean_preprocessor_cache()"
]
},
{
"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. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = remote_run.get_output()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Best Model based on any other metric"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# lookup_metric = \"accuracy\"\n",
"# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model from a specific iteration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# iteration = 1\n",
"# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load test data.\n",
"from pandas_ml import ConfusionMatrix\n",
"\n",
"data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
" shuffle = True, random_state = 42,\n",
" remove = remove)\n",
"\n",
"X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
"y_test = data_test.target\n",
"\n",
"# Test our best pipeline.\n",
"\n",
"y_pred = fitted_model.predict(X_test)\n",
"y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
"y_test_strings = [data_test.target_names[i] for i in y_test]\n",
"\n",
"cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
"print(cm)\n",
"cm.plot()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

507
how-to-use-azureml/automated-machine-learning/remote-execution/auto-ml-remote-execution.ipynb Normal file
View File

@@ -0,0 +1,507 @@
{
"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": [
"# Automated Machine Learning: Remote Execution using DSVM (Ubuntu)\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",
"\n",
"In this notebook you wiil learn how to:\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Attach an existing DSVM to a workspace.\n",
"3. Configure AutoML using `AutoMLConfig`.\n",
"4. Train the model using the DSVM.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"In addition, this notebook showcases the following features:\n",
"- **Parallel** executions for iterations\n",
"- **Asynchronous** tracking of progress\n",
"- **Cancellation** of individual iterations or the entire run\n",
"- Retrieving models for any iteration or logged metric\n",
"- Specifying AutoML settings as `**kwargs`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"import os\n",
"import random\n",
"import time\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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the run history container in the workspace.\n",
"experiment_name = 'automl-remote-dsvm4'\n",
"project_folder = './sample_projects/automl-remote-dsvm4'\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": [
"## 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": [
"## Create a Remote Linux DSVM\n",
"**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import DsvmCompute\n",
"\n",
"dsvm_name = 'mydsvma'\n",
"try:\n",
" dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
" print('Found an existing DSVM.')\n",
"except:\n",
" print('Creating a new DSVM.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
" dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
" dsvm_compute.wait_for_completion(show_output = True)\n",
" print(\"Waiting one minute for ssh to be accessible\")\n",
" time.sleep(60) # Wait for ssh to be accessible"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"conda_run_config.target = dsvm_compute\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Get Data File\n",
"For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
"In this example, the `get_data()` function returns data using 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": [
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $project_folder/get_data.py\n",
"\n",
"from sklearn import datasets\n",
"from scipy import sparse\n",
"import numpy as np\n",
"\n",
"def get_data():\n",
" \n",
" digits = datasets.load_digits()\n",
" X_train = digits.data[100:,:]\n",
" y_train = digits.target[100:]\n",
"\n",
" return { \"X\" : X_train, \"y\" : y_train }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML <a class=\"anchor\" id=\"Instantiate-AutoML-Remote-DSVM\"></a>\n",
"\n",
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
"\n",
"**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
"\n",
"|Property|Description|\n",
"|-|-|\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the DSVM.|"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 10,\n",
" \"iterations\": 20,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": False,\n",
" \"max_concurrent_iterations\": 2,\n",
" \"verbosity\": logging.INFO\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder, \n",
" run_configuration=conda_run_config,\n",
" data_script = project_folder + \"/get_data.py\",\n",
" **automl_settings\n",
" )\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
"\n",
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Results\n",
"\n",
"#### Loading Executed Runs\n",
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"remote_run = AutoMLRun(experiment=experiment, run_id = 'AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
]
},
{
"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",
"You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\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(remote_run).show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Wait until the run finishes.\n",
"remote_run.wait_for_completion(show_output = True)"
]
},
{
"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(remote_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": [
"## Cancelling Runs\n",
"\n",
"You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cancel the ongoing experiment and stop scheduling new iterations.\n",
"# remote_run.cancel()\n",
"\n",
"# Cancel iteration 1 and move onto iteration 2.\n",
"# remote_run.cancel_iteration(1)"
]
},
{
"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 = remote_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 which has the smallest `log_loss` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lookup_metric = \"log_loss\"\n",
"best_run, fitted_model = remote_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 = remote_run.get_output(iteration = iteration)\n",
"print(third_run)\n",
"print(third_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\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": [
"#### Test Our Best Fitted Model"
]
},
{
"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()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

251
how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb Normal file
View File

@@ -0,0 +1,251 @@
{
"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": [
"# Automated Machine Learning: Sample Weight\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 sample weight with AutoML. Sample weight is used where some sample values are more important than others.\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose names for the regular and the sample weight experiments.\n",
"experiment_name = 'non_sample_weight_experiment'\n",
"sample_weight_experiment_name = 'sample_weight_experiment'\n",
"\n",
"project_folder = './sample_projects/automl-local-classification'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"sample_weight_experiment=Experiment(ws, sample_weight_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": [
"## 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": [
"## Configure AutoML\n",
"\n",
"Instantiate two `AutoMLConfig` objects. One will be used with `sample_weight` and one without."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits = datasets.load_digits()\n",
"X_train = digits.data[100:,:]\n",
"y_train = digits.target[100:]\n",
"\n",
"# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
"# This makes the model more likely to classify as 4 if the image it not clear.\n",
"sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_train])\n",
"\n",
"automl_classifier = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" primary_metric = 'AUC_weighted',\n",
" iteration_timeout_minutes = 60,\n",
" iterations = 10,\n",
" n_cross_validations = 2,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)\n",
"\n",
"automl_sample_weight = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" primary_metric = 'AUC_weighted',\n",
" iteration_timeout_minutes = 60,\n",
" iterations = 10,\n",
" n_cross_validations = 2,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" sample_weight = sample_weight,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"Call the `submit` method on the experiment objects 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_classifier, show_output = True)\n",
"sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output = True)\n",
"\n",
"best_run, fitted_model = local_run.get_output()\n",
"best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the Best Fitted Model\n",
"\n",
"#### Load Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"digits = datasets.load_digits()\n",
"X_test = digits.data[:100, :]\n",
"y_test = digits.target[:100]\n",
"images = digits.images[:100]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compare the Models\n",
"The prediction from the sample weight model is more likely to correctly predict 4's. However, it is also more likely to predict 4 for some images that are not labelled as 4."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Randomly select digits and test.\n",
"for index in range(0,len(y_test)):\n",
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
" predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
" label = y_test[index]\n",
" if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
" title = \"Label value = %d Predicted value = %d Prediced with sample weight = %d\" % (label, predicted, predicted_sample_weight)\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()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

384
how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb Normal file
View File

@@ -0,0 +1,384 @@
{
"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": [
"# Automated Machine Learning: Train Test Split and Handling Sparse Data\n",
"\n",
"In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\n",
"\n",
"Make sure you have executed the [configuration](../configuration.ipynb) before running this notebook.\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",
"4. Train the model.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"In addition this notebook showcases the following features\n",
"- Explicit train test splits \n",
"- Handling **sparse data** in the input"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# choose a name for the experiment\n",
"experiment_name = 'automl-local-missing-data'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-missing-data'\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": [
"## 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": [
"## Creating Sparse Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_20newsgroups\n",
"from sklearn.feature_extraction.text import HashingVectorizer\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"remove = ('headers', 'footers', 'quotes')\n",
"categories = [\n",
" 'alt.atheism',\n",
" 'talk.religion.misc',\n",
" 'comp.graphics',\n",
" 'sci.space',\n",
"]\n",
"data_train = fetch_20newsgroups(subset = 'train', categories = categories,\n",
" shuffle = True, random_state = 42,\n",
" remove = remove)\n",
"\n",
"X_train, X_valid, y_train, y_valid = train_test_split(data_train.data, data_train.target, test_size = 0.33, random_state = 42)\n",
"\n",
"\n",
"vectorizer = HashingVectorizer(stop_words = 'english', alternate_sign = False,\n",
" n_features = 2**16)\n",
"X_train = vectorizer.transform(X_train)\n",
"X_valid = vectorizer.transform(X_valid)\n",
"\n",
"summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
"summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
"summary_df['Validation Set'] = [X_valid.shape[0], X_valid.shape[1]]\n",
"summary_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\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>average_precision_score_weighted</i><br><i>norm_macro_recall</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",
"|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *True*.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
"|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
"|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification for the custom validation set.|\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.|"
]
},
{
"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 = 5,\n",
" preprocess = False,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" X_valid = X_valid, \n",
" y_valid = y_valid, \n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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": "markdown",
"metadata": {},
"source": [
"## Explore the 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()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Best Model Based on Any Other Metric\n",
"Show the run and the model which has the smallest `accuracy` value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# lookup_metric = \"accuracy\"\n",
"# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
]
},
{
"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",
"# best_run, fitted_model = local_run.get_output(iteration = iteration)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing the Best Fitted Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load test data.\n",
"from pandas_ml import ConfusionMatrix\n",
"\n",
"data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
" shuffle = True, random_state = 42,\n",
" remove = remove)\n",
"\n",
"X_test = vectorizer.transform(data_test.data)\n",
"y_test = data_test.target\n",
"\n",
"# Test our best pipeline.\n",
"\n",
"y_pred = fitted_model.predict(X_test)\n",
"y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
"y_test_strings = [data_test.target_names[i] for i in y_test]\n",
"\n",
"cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
"print(cm)\n",
"cm.plot()"
]
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"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
}

264
how-to-use-azureml/azure-databricks/01.Installation_and_Configuration.ipynb Normal file
View File

@@ -0,0 +1,264 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
"\n",
"**azureml-sdk**\n",
"* Source: Upload Python Egg or PyPi\n",
"* PyPi Name: `azureml-sdk[databricks]`\n",
"* Select Install Library"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"# Check core SDK version number - based on build number of preview/master.\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image2b.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
"\n",
"You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
"\n",
"For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
"\n",
"NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# subscription_id = \"<your-subscription-id>\"\n",
"# resource_group = \"<your-existing-resource-group>\"\n",
"# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
"# workspace_region = \"<your-resource group-region>\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"subscription_id = dbutils.widgets.get('subscription_id')\n",
"resource_group = dbutils.widgets.get('resource_group')\n",
"workspace_name = dbutils.widgets.get('workspace_name')\n",
"workspace_region = dbutils.widgets.get('workspace_region')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import the Workspace class and check the azureml SDK version\n",
"# exist_ok checks if workspace exists or not.\n",
"\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.create(name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group, \n",
" location = workspace_region,\n",
" auth = auth,\n",
" exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"## import the Workspace class and check the azureml SDK version\n",
"## exist_ok checks if workspace exists or not.\n",
"#\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace.create(name = workspace_name,\n",
"# subscription_id = subscription_id,\n",
"# resource_group = resource_group, \n",
"# location = workspace_region,\n",
"# exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get workspace details\n",
"ws.get_details()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"ws = Workspace(workspace_name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group,\n",
" auth = auth)\n",
"\n",
"# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"ws.write_config()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#ws = Workspace(workspace_name = workspace_name,\n",
"# subscription_id = subscription_id,\n",
"# resource_group = resource_group)\n",
"#\n",
"## persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"#ws.write_config()\n",
"###if you need to give a different path/filename please use this\n",
"###write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"help(Workspace)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import the Workspace class and check the azureml SDK version\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config(auth = auth)\n",
"#ws = Workspace.from_config(<full path>)\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"## import the Workspace class and check the azureml SDK version\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace.from_config()\n",
"##ws = Workspace.from_config(<full path>)\n",
"#print('Workspace name: ' + ws.name, \n",
"# 'Azure region: ' + ws.location, \n",
"# 'Subscription id: ' + ws.subscription_id, \n",
"# 'Resource group: ' + ws.resource_group, sep = '\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"name": "01.Installation_and_Configuration",
"notebookId": 3836944406456490
},
"nbformat": 4,
"nbformat_minor": 1
}

182
how-to-use-azureml/azure-databricks/02.Ingest_data.ipynb Normal file
View File

@@ -0,0 +1,182 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image1.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Data Ingestion"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import urllib"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
"basedataurl = \"https://amldockerdatasets.azureedge.net\"\n",
"datafile = \"AdultCensusIncome.csv\"\n",
"datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
"\n",
"if os.path.isfile(datafile_dbfs):\n",
" print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
"else:\n",
" print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
" urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create a Spark dataframe out of the csv file.\n",
"data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
"print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
"data_all.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#renaming columns\n",
"columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
"data_all = data_all.toDF(*columns_new)\n",
"data_all.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display(data_all.limit(5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Data Preparation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Choose feature columns and the label column.\n",
"label = \"income\"\n",
"xvars = set(data_all.columns) - {label}\n",
"\n",
"print(\"label = {}\".format(label))\n",
"print(\"features = {}\".format(xvars))\n",
"\n",
"data = data_all.select([*xvars, label])\n",
"\n",
"# Split data into train and test.\n",
"train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
"\n",
"print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
"print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Data Persistence"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Write the train and test data sets to intermediate storage\n",
"train_data_path = \"AdultCensusIncomeTrain\"\n",
"test_data_path = \"AdultCensusIncomeTest\"\n",
"\n",
"train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
"test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
"\n",
"train.write.mode('overwrite').parquet(train_data_path)\n",
"test.write.mode('overwrite').parquet(test_data_path)\n",
"print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"name": "02.Ingest_data",
"notebookId": 3836944406456362
},
"nbformat": 4,
"nbformat_minor": 1
}

396
how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb Normal file
View File

@@ -0,0 +1,396 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image2.JPG)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Building"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pprint\n",
"import numpy as np\n",
"\n",
"from pyspark.ml import Pipeline, PipelineModel\n",
"from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
"from pyspark.ml.classification import LogisticRegression\n",
"from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
"from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# import the Workspace class and check the azureml SDK version\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config(auth = auth)\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"## import the Workspace class and check the azureml SDK version\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace.from_config()\n",
"#print('Workspace name: ' + ws.name, \n",
"# 'Azure region: ' + ws.location, \n",
"# 'Subscription id: ' + ws.subscription_id, \n",
"# 'Resource group: ' + ws.resource_group, sep = '\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#get the train and test datasets\n",
"train_data_path = \"AdultCensusIncomeTrain\"\n",
"test_data_path = \"AdultCensusIncomeTest\"\n",
"\n",
"train = spark.read.parquet(train_data_path)\n",
"test = spark.read.parquet(test_data_path)\n",
"\n",
"print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
"print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
"\n",
"train.printSchema()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Define Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = \"income\"\n",
"dtypes = dict(train.dtypes)\n",
"dtypes.pop(label)\n",
"\n",
"si_xvars = []\n",
"ohe_xvars = []\n",
"featureCols = []\n",
"for idx,key in enumerate(dtypes):\n",
" if dtypes[key] == \"string\":\n",
" featureCol = \"-\".join([key, \"encoded\"])\n",
" featureCols.append(featureCol)\n",
" \n",
" tmpCol = \"-\".join([key, \"tmp\"])\n",
" # string-index and one-hot encode the string column\n",
" #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
" #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
" #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
" #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
" si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
" ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
" else:\n",
" featureCols.append(key)\n",
"\n",
"# string-index the label column into a column named \"label\"\n",
"si_label = StringIndexer(inputCol=label, outputCol='label')\n",
"\n",
"# assemble the encoded feature columns in to a column named \"features\"\n",
"assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.run import Run\n",
"from azureml.core.experiment import Experiment\n",
"import numpy as np\n",
"import os\n",
"import shutil\n",
"\n",
"model_name = \"AdultCensus_runHistory.mml\"\n",
"model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
"run_history_name = 'spark-ml-notebook'\n",
"\n",
"# start a training run by defining an experiment\n",
"myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
"root_run = myexperiment.start_logging()\n",
"\n",
"# Regularization Rates - \n",
"regs = [0.0001, 0.001, 0.01, 0.1]\n",
" \n",
"# try a bunch of regularization rate in a Logistic Regression model\n",
"for reg in regs:\n",
" print(\"Regularization rate: {}\".format(reg))\n",
" # create a bunch of child runs\n",
" with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
" # create a new Logistic Regression model.\n",
" lr = LogisticRegression(regParam=reg)\n",
" \n",
" # put together the pipeline\n",
" pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
"\n",
" # train the model\n",
" model_p = pipe.fit(train)\n",
" \n",
" # make prediction\n",
" pred = model_p.transform(test)\n",
" \n",
" # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
" bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
" au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
" au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
"\n",
" print(\"Area under ROC: {}\".format(au_roc))\n",
" print(\"Area Under PR: {}\".format(au_prc))\n",
" \n",
" # log reg, au_roc, au_prc and feature names in run history\n",
" run.log(\"reg\", reg)\n",
" run.log(\"au_roc\", au_roc)\n",
" run.log(\"au_prc\", au_prc)\n",
" run.log_list(\"columns\", train.columns)\n",
"\n",
" # save model\n",
" model_p.write().overwrite().save(model_name)\n",
" \n",
" # upload the serialized model into run history record\n",
" mdl, ext = model_name.split(\".\")\n",
" model_zip = mdl + \".zip\"\n",
" shutil.make_archive(mdl, 'zip', model_dbfs)\n",
" run.upload_file(\"outputs/\" + model_name, model_zip) \n",
" #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
"\n",
" # now delete the serialized model from local folder since it is already uploaded to run history \n",
" shutil.rmtree(model_dbfs)\n",
" os.remove(model_zip)\n",
" \n",
"# Declare run completed\n",
"root_run.complete()\n",
"root_run_id = root_run.id\n",
"print (\"run id:\", root_run.id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"metrics = root_run.get_metrics(recursive=True)\n",
"best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
"print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Get the best run\n",
"child_runs = {}\n",
"\n",
"for r in root_run.get_children():\n",
" child_runs[r.id] = r\n",
" \n",
"best_run = child_runs[best_run_id]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Download the model from the best run to a local folder\n",
"best_model_file_name = \"best_model.zip\"\n",
"best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##unzip the model to dbfs (as load() seems to require that) and load it.\n",
"if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
" shutil.rmtree(model_dbfs)\n",
"shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
"\n",
"model_p_best = PipelineModel.load(model_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# make prediction\n",
"pred = model_p_best.transform(test)\n",
"output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
"display(output.limit(5))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
"bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
"au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
"au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
"\n",
"print(\"Area under ROC: {}\".format(au_roc))\n",
"print(\"Area Under PR: {}\".format(au_prc))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Model Persistence"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
"model_p_best.write().overwrite().save(model_name)\n",
"print(\"saved model to {}\".format(model_dbfs))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%sh\n",
"\n",
"ls -la /dbfs/AdultCensus_runHistory.mml/*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dbutils.notebook.exit(\"success\")"
]
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"name": "03.Build_model_runHistory",
"notebookId": 3836944406456339
},
"nbformat": 4,
"nbformat_minor": 1
}

354
how-to-use-azureml/azure-databricks/04.Deploy_to_ACI.ipynb Normal file
View File

@@ -0,0 +1,354 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
"\n",
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please ensure you have run all previous notebooks in sequence before running this.\n",
"\n",
"Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![04ACI](files/tables/image3.JPG)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"#'''\n",
"ws = Workspace.from_config(auth = auth)\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep = '\\n')\n",
"#'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#from azureml.core import Workspace\n",
"#import azureml.core\n",
"#\n",
"## Check core SDK version number\n",
"#print(\"SDK version:\", azureml.core.VERSION)\n",
"#\n",
"##'''\n",
"#ws = Workspace.from_config()\n",
"#print('Workspace name: ' + ws.name, \n",
"# 'Azure region: ' + ws.location, \n",
"# 'Subscription id: ' + ws.subscription_id, \n",
"# 'Resource group: ' + ws.resource_group, sep = '\\n')\n",
"##'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##NOTE: service deployment always gets the model from the current working dir.\n",
"import os\n",
"\n",
"model_name = \"AdultCensus_runHistory.mml\" # \n",
"model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
"\n",
"print(\"copy model from dbfs to local\")\n",
"model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
"dbutils.fs.cp(model_name, model_local, True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"mymodel = Model.register(model_path = model_name, # this points to a local file\n",
" model_name = model_name, # this is the name the model is registered as, am using same name for both path and name. \n",
" description = \"ADB trained model by Parashar\",\n",
" workspace = ws)\n",
"\n",
"print(mymodel.name, mymodel.description, mymodel.version)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#%%writefile score_sparkml.py\n",
"score_sparkml = \"\"\"\n",
" \n",
"import json\n",
" \n",
"def init():\n",
" # One-time initialization of PySpark and predictive model\n",
" import pyspark\n",
" from azureml.core.model import Model\n",
" from pyspark.ml import PipelineModel\n",
" \n",
" global trainedModel\n",
" global spark\n",
" \n",
" spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
" model_name = \"{model_name}\" #interpolated\n",
" model_path = Model.get_model_path(model_name)\n",
" trainedModel = PipelineModel.load(model_path)\n",
" \n",
"def run(input_json):\n",
" if isinstance(trainedModel, Exception):\n",
" return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
" \n",
" try:\n",
" sc = spark.sparkContext\n",
" input_list = json.loads(input_json)\n",
" input_rdd = sc.parallelize(input_list)\n",
" input_df = spark.read.json(input_rdd)\n",
" \n",
" # Compute prediction\n",
" prediction = trainedModel.transform(input_df)\n",
" #result = prediction.first().prediction\n",
" predictions = prediction.collect()\n",
" \n",
" #Get each scored result\n",
" preds = [str(x['prediction']) for x in predictions]\n",
" result = \",\".join(preds)\n",
" # you can return any data type as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" result = str(e)\n",
" return result\n",
" \n",
"\"\"\".format(model_name=model_name)\n",
" \n",
"exec(score_sparkml)\n",
" \n",
"with open(\"score_sparkml.py\", \"w\") as file:\n",
" file.write(score_sparkml)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
"\n",
"with open(\"mydeployenv.yml\",\"w\") as f:\n",
" f.write(myacienv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#deploy to ACI\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# this will take 10-15 minutes to finish\n",
"\n",
"service_name = \"aciws\"\n",
"runtime = \"spark-py\" \n",
"driver_file = \"score_sparkml.py\"\n",
"my_conda_file = \"mydeployenv.yml\"\n",
"\n",
"# image creation\n",
"from azureml.core.image import ContainerImage\n",
"myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
" runtime = runtime, \n",
" conda_file = my_conda_file)\n",
"\n",
"# Webservice creation\n",
"myservice = Webservice.deploy_from_model(\n",
" workspace=ws, \n",
" name=service_name,\n",
" deployment_config = myaci_config,\n",
" models = [mymodel],\n",
" image_config = myimage_config\n",
" )\n",
"\n",
"myservice.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"help(Webservice)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# List images by ws\n",
"\n",
"for i in ContainerImage.list(workspace = ws):\n",
" print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#for using the Web HTTP API \n",
"print(myservice.scoring_uri)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"#get the some sample data\n",
"test_data_path = \"AdultCensusIncomeTest\"\n",
"test = spark.read.parquet(test_data_path).limit(5)\n",
"\n",
"test_json = json.dumps(test.toJSON().collect())\n",
"\n",
"print(test_json)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#using data defined above predict if income is >50K (1) or <=50K (0)\n",
"myservice.run(input_data=test_json)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#comment to not delete the web service\n",
"#myservice.delete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "pasha"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"name": "04.DeploytoACI",
"notebookId": 3836944406456376
},
"nbformat": 4,
"nbformat_minor": 1
}

634
how-to-use-azureml/azure-databricks/06.AutoML_Databricks_local.ipynb Normal file
View File

@@ -0,0 +1,634 @@
{
"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 : Classification with Local Compute on Azure DataBricks\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",
"In this notebook you will learn how to:\n",
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
"2. Create an `Experiment` in an existing `Workspace`.\n",
"3. Configure AutoML using `AutoMLConfig`.\n",
"4. Train the model using AzureDataBricks.\n",
"5. Explore the results.\n",
"6. Test the best fitted model.\n",
"\n",
"Prerequisites:\n",
"Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register Machine Learning Services Resource Provider\n",
"Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
"Start the Azure portal.\n",
"Select your All services and then Subscription.\n",
"Select the subscription that you want to use.\n",
"Click on Resource providers\n",
"Click the Register link next to Microsoft.MachineLearningServices"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the Azure ML Core SDK Version to Validate Your Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"\n",
"print(\"SDK Version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize an Azure ML Workspace\n",
"### What is an Azure ML Workspace and Why Do I Need One?\n",
"\n",
"An Azure ML workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows. In particular, an Azure ML workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
"\n",
"\n",
"### What do I Need?\n",
"\n",
"To create or access an Azure ML workspace, you will need to import the Azure ML library and specify following information:\n",
"* A name for your workspace. You can choose one.\n",
"* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
"* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [Azure portal](https://portal.azure.com)\n",
"* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#subscription_id = \"<Your SubscriptionId>\"\n",
"#resource_group = \"<Resource group - new or existing>\"\n",
"#workspace_name = \"<workspace to be created>\"\n",
"#workspace_region = \"<azureregion>\" #eg. eastus2, westcentralus, westeurope"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a Workspace\n",
"If you already have access to an Azure ML workspace you want to use, you can skip this cell. Otherwise, this cell will create an Azure ML workspace for you in the specified subscription, provided you have the correct permissions for the given `subscription_id`.\n",
"\n",
"This will fail when:\n",
"1. The workspace already exists.\n",
"2. You do not have permission to create a workspace in the resource group.\n",
"3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription.\n",
"\n",
"If workspace creation fails for any reason other than already existing, please work with your IT administrator to provide you with the appropriate permissions or to provision the required resources.\n",
"\n",
"**Note:** Creation of a new workspace can take several minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"# import auth creds from notebook parameters\n",
"tenant = dbutils.widgets.get('tenant_id')\n",
"username = dbutils.widgets.get('service_principal_id')\n",
"password = dbutils.widgets.get('service_principal_password')\n",
"\n",
"auth = azureml.core.authentication.ServicePrincipalAuthentication(tenant, username, password)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"subscription_id = dbutils.widgets.get('subscription_id')\n",
"resource_group = dbutils.widgets.get('resource_group')\n",
"workspace_name = dbutils.widgets.get('workspace_name')\n",
"workspace_region = dbutils.widgets.get('workspace_region')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Import the Workspace class and check the Azure ML SDK version.\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.create(name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group, \n",
" location = workspace_region,\n",
" auth = auth,\n",
" exist_ok=True)\n",
"ws.get_details()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#from azureml.core import Workspace\n",
"#import azureml.core\n",
"#\n",
"## Check core SDK version number\n",
"#print(\"SDK version:\", azureml.core.VERSION)\n",
"#\n",
"##'''\n",
"#ws = Workspace.from_config()\n",
"#print('Workspace name: ' + ws.name, \n",
"# 'Azure region: ' + ws.location, \n",
"# 'Subscription id: ' + ws.subscription_id, \n",
"# 'Resource group: ' + ws.resource_group, sep = '\\n')\n",
"##'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configuring Your Local Environment\n",
"You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace(workspace_name = workspace_name,\n",
" subscription_id = subscription_id,\n",
" resource_group = resource_group,\n",
" auth = auth)\n",
"\n",
"# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"ws.write_config()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#from azureml.core import Workspace\n",
"#\n",
"#ws = Workspace(workspace_name = workspace_name,\n",
"# subscription_id = subscription_id,\n",
"# resource_group = resource_group)\n",
"#\n",
"## Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
"#ws.write_config()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Folder to Host Sample Projects\n",
"Finally, create a folder where all the sample projects will be hosted."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"sample_projects_folder = './sample_projects'\n",
"\n",
"if not os.path.isdir(sample_projects_folder):\n",
" os.mkdir(sample_projects_folder)\n",
" \n",
"print('Sample projects will be created in {}.'.format(sample_projects_folder))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create an Experiment\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",
"import os\n",
"import random\n",
"import time\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",
"\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,
"metadata": {},
"outputs": [],
"source": [
"##TESTONLY\n",
"ws = Workspace.from_config(auth = auth)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##PUBLISHONLY\n",
"#ws = Workspace.from_config(auth = auth)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Choose a name for the experiment and specify the project folder.\n",
"experiment_name = 'automl-local-classification'\n",
"project_folder = './sample_projects/automl-local-classification'\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": [
"## 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": [
"## Load Training Data Using DataPrep"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
"# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
"simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
"X_train = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1) # Remove the header row.\n",
"\n",
"# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
"# and convert column types manually.\n",
"# Here we read a comma delimited file and convert all columns to integers.\n",
"y_train = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review the Data Preparation Result\n",
"You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_train.skip(1).head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure AutoML\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>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</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",
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
"|**max_cuncurrent_iterations**|Maximum number of iterations to execute in parallel. This should be less than the number of cores on the ADB..|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\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",
"|**concurrent_iterations**|number of concurrent runs <= total cores in all worker nodes in your Databricks cluster|\n",
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
]
},
{
"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 = 10,\n",
" iterations = 30,\n",
" n_cross_validations = 10,\n",
" max_concurrent_iterations = 8, #change it based on number of cores in worker nodes\n",
" verbosity = logging.INFO,\n",
" spark_context=sc, #databricks/spark related\n",
" X = X_train, \n",
" y = y_train,\n",
" enable_cache=False,\n",
" path = project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Models\n",
"\n",
"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) # for higher runs please use show_output=False and use the below"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Portal URL for Monitoring Runs\n",
"\n",
"The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(local_run.get_portal_url())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following will show the child runs and waits for the parent run to complete."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Retrieve All Child Runs after the experiment is completed (in portal)\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 after the above run is complete \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 after the above run is complete based on the child run\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": [
"### Test the Best Fitted Model\n",
"\n",
"#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"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 digits and see how our model works. This is just an example to show you."
]
},
{
"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",
" display(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "savitam"
},
{
"name": "wamartin"
}
],
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
},
"name": "auto-ml-classification-local-adb",
"notebookId": 3836944406456411
},
"nbformat": 4,
"nbformat_minor": 1
}