05A - Working with Environments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Environments\n",
    "\n",
    "By now you've run many experiments in your Azure Machine Learning workspace, and in some cases you've had to specify the particular Python packages required in the environment where the experiment code is run. In this lab, you'll explore environments in a little more detail.\n",
    "\n",
    "## Connect to Your Workspace\n",
    "\n",
    "The first thing you need to do is to connect to your workspace using the Azure ML SDK.\n",
    "\n",
    "> **Note**: If the authenticated session with your Azure subscription has expired since you completed the previous exercise, you'll be prompted to reauthenticate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "from azureml.core import Workspace\n",
    "\n",
    "# Load the workspace from the saved config file\n",
    "ws = Workspace.from_config()\n",
    "print('Ready to use Azure ML {} to work with {}'.format(azureml.core.VERSION, ws.name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data for an Experiment\n",
    "\n",
    "In this lab, you'll use a dataset containing details of diabetes patients. Run the cell below to create this dataset (if you created it in the previous lab, the code will create a new version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Dataset\n",
    "\n",
    "default_ds = ws.get_default_datastore()\n",
    "default_ds.upload_files(files=['./data/diabetes.csv', './data/diabetes2.csv'], # Upload the diabetes csv files in /data\n",
    "                       target_path='diabetes-data/', # Put it in a folder path in the datastore\n",
    "                       overwrite=True, # Replace existing files of the same name\n",
    "                       show_progress=True)\n",
    "\n",
    "#Create a tabular dataset from the path on the datastore (this may take a short while)\n",
    "tab_data_set = Dataset.Tabular.from_delimited_files(path=(default_ds, 'diabetes-data/*.csv'))\n",
    "\n",
    "# Register the tabular dataset\n",
    "tab_data_set = tab_data_set.register(workspace=ws, \n",
    "                           name='diabetes dataset',\n",
    "                           description='diabetes data',\n",
    "                           tags = {'format':'CSV'},\n",
    "                           create_new_version=True)\n",
    "\n",
    "print('Dataset ready.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Training Script\n",
    "\n",
    "Run the following two cells to create:\n",
    "1. A folder for a new experiment\n",
    "2. An training script file that uses **scikit-learn** to train a model and **matplotlib** to plot a ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Create a folder for the experiment files\n",
    "experiment_folder = 'diabetes_training_logistic'\n",
    "os.makedirs(experiment_folder, exist_ok=True)\n",
    "print(experiment_folder, 'folder created')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $experiment_folder/diabetes_training.py\n",
    "# Import libraries\n",
    "import argparse\n",
    "from azureml.core import Run\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set regularization hyperparameter (passed as an argument to the script)\n",
    "parser = argparse.ArgumentParser()\n",
    "parser.add_argument('--regularization', type=float, dest='reg_rate', default=0.01, help='regularization rate')\n",
    "args = parser.parse_args()\n",
    "reg = args.reg_rate\n",
    "\n",
    "# Get the experiment run context\n",
    "run = Run.get_context()\n",
    "\n",
    "# load the diabetes data (passed as an input dataset)\n",
    "print(\"Loading Data...\")\n",
    "diabetes = run.input_datasets['diabetes'].to_pandas_dataframe()\n",
    "\n",
    "# Separate features and labels\n",
    "X, y = diabetes[['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']].values, diabetes['Diabetic'].values\n",
    "\n",
    "# Split data into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
    "\n",
    "# Train a logistic regression model\n",
    "print('Training a logistic regression model with regularization rate of', reg)\n",
    "run.log('Regularization Rate',  np.float(reg))\n",
    "model = LogisticRegression(C=1/reg, solver=\"liblinear\").fit(X_train, y_train)\n",
    "\n",
    "# calculate accuracy\n",
    "y_hat = model.predict(X_test)\n",
    "acc = np.average(y_hat == y_test)\n",
    "print('Accuracy:', acc)\n",
    "run.log('Accuracy', np.float(acc))\n",
    "\n",
    "# calculate AUC\n",
    "y_scores = model.predict_proba(X_test)\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))\n",
    "run.log('AUC', np.float(auc))\n",
    "\n",
    "# plot ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "run.log_image(name = \"ROC\", plot = fig)\n",
    "plt.show()\n",
    "\n",
    "os.makedirs('outputs', exist_ok=True)\n",
    "# note file saved in the outputs folder is automatically uploaded into experiment record\n",
    "joblib.dump(value=model, filename='outputs/diabetes_model.pkl')\n",
    "\n",
    "run.complete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define an Environment\n",
    "\n",
    "When you run a Python script as an experiment in Azure Machine Learning, a Conda environment is created to define the execution context for the script. Azure Machine Learning provides a default environment that includes many common packages; including the **azureml-defaults** package that contains the libraries necessary for working with an experiment run, as well as popular packages like **pandas** and **numpy**.\n",
    "\n",
    "You can also define your own environment and add packages by using **conda** or **pip**, to ensure your experiment has access to all the libraries it requires. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# Create a Python environment for the experiment\n",
    "diabetes_env = Environment(\"diabetes-experiment-env\")\n",
    "diabetes_env.python.user_managed_dependencies = False # Let Azure ML manage dependencies\n",
    "diabetes_env.docker.enabled = True # Use a docker container\n",
    "\n",
    "# Create a set of package dependencies (conda or pip as required)\n",
    "diabetes_packages = CondaDependencies.create(conda_packages=['scikit-learn','ipykernel','matplotlib', 'pandas'],\n",
    "                                          pip_packages=['azureml-sdk','pyarrow'])\n",
    "\n",
    "# Add the dependencies to the environment\n",
    "diabetes_env.python.conda_dependencies = diabetes_packages\n",
    "\n",
    "print(diabetes_env.name, 'defined.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can use the environment for the experiment by assigning it to an Estimator (or RunConfig).\n",
    "\n",
    "The following code assigns the environment you created to a generic estimator, and submits an experiment. As the experiment runs, observe the run details in the widget and in the **azureml_logs/60_control_log.txt** output log, you'll see the conda environment being built."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.estimator import Estimator\n",
    "from azureml.core import Experiment\n",
    "from azureml.widgets import RunDetails\n",
    "\n",
    "# Set the script parameters\n",
    "script_params = {\n",
    "    '--regularization': 0.1\n",
    "}\n",
    "\n",
    "# Get the training dataset\n",
    "diabetes_ds = ws.datasets.get(\"diabetes dataset\")\n",
    "\n",
    "# Create an estimator\n",
    "estimator = Estimator(source_directory=experiment_folder,\n",
    "              inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input\n",
    "              script_params=script_params,\n",
    "              compute_target = 'local',\n",
    "              environment_definition = diabetes_env,\n",
    "              entry_script='diabetes_training.py')\n",
    "\n",
    "# Create an experiment\n",
    "experiment = Experiment(workspace = ws, name = 'diabetes-training')\n",
    "\n",
    "# Run the experiment\n",
    "run = experiment.submit(config=estimator)\n",
    "# Show the run details while running\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experiment successfully used the environment, which included all of the packages it required - you can view the metrics and outputs from the experiment run in Azure Machine Learning Studio, or by running the code below - including the model trained using **scikit-learn** and the ROC chart image generated using **matplotlib**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get logged metrics\n",
    "metrics = run.get_metrics()\n",
    "for key in metrics.keys():\n",
    "        print(key, metrics.get(key))\n",
    "print('\\n')\n",
    "for file in run.get_file_names():\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the Environment\n",
    "\n",
    "Having gone to the trouble of defining an environment with the packages you need, you can register it in the workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Register the environment\n",
    "diabetes_env.register(workspace=ws)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the environment is registered with the name you assigned when you first created it (in this case, *diabetes-experiment-env*).\n",
    "\n",
    "With the environment registered, you can reuse it for any scripts that have the same requirements. For example, let's create a folder and script to train a diabetes model using a different algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Create a folder for the experiment files\n",
    "experiment_folder = 'diabetes_training_tree'\n",
    "os.makedirs(experiment_folder, exist_ok=True)\n",
    "print(experiment_folder, 'folder created')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $experiment_folder/diabetes_training.py\n",
    "# Import libraries\n",
    "from azureml.core import Run\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import joblib\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the experiment run context\n",
    "run = Run.get_context()\n",
    "\n",
    "# load the diabetes data (passed as an input dataset)\n",
    "print(\"Loading Data...\")\n",
    "diabetes = run.input_datasets['diabetes'].to_pandas_dataframe()\n",
    "\n",
    "# Separate features and labels\n",
    "X, y = diabetes[['Pregnancies','PlasmaGlucose','DiastolicBloodPressure','TricepsThickness','SerumInsulin','BMI','DiabetesPedigree','Age']].values, diabetes['Diabetic'].values\n",
    "\n",
    "# Split data into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
    "\n",
    "# Train a decision tree model\n",
    "print('Training a decision tree model')\n",
    "model = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "\n",
    "# calculate accuracy\n",
    "y_hat = model.predict(X_test)\n",
    "acc = np.average(y_hat == y_test)\n",
    "print('Accuracy:', acc)\n",
    "run.log('Accuracy', np.float(acc))\n",
    "\n",
    "# calculate AUC\n",
    "y_scores = model.predict_proba(X_test)\n",
    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
    "print('AUC: ' + str(auc))\n",
    "run.log('AUC', np.float(auc))\n",
    "\n",
    "# plot ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "# Plot the diagonal 50% line\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "# Plot the FPR and TPR achieved by our model\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "run.log_image(name = \"ROC\", plot = fig)\n",
    "plt.show()\n",
    "\n",
    "os.makedirs('outputs', exist_ok=True)\n",
    "# note file saved in the outputs folder is automatically uploaded into experiment record\n",
    "joblib.dump(value=model, filename='outputs/diabetes_model.pkl')\n",
    "\n",
    "run.complete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can retrieve the registered environment and use it to configure the estimator for a new experiment that runs the alternative training script (there are no script parameters this time because a Decision Tree classifier doesn't require any hyperparameter values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.estimator import Estimator\n",
    "from azureml.core import Environment, Experiment\n",
    "from azureml.widgets import RunDetails\n",
    "\n",
    "registered_env = Environment.get(ws, 'diabetes-experiment-env')\n",
    "\n",
    "# Get the training dataset\n",
    "diabetes_ds = ws.datasets.get(\"diabetes dataset\")\n",
    "\n",
    "# Create an estimator\n",
    "estimator = Estimator(source_directory=experiment_folder,\n",
    "              inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input\n",
    "              compute_target = 'local',\n",
    "              environment_definition = registered_env,\n",
    "              entry_script='diabetes_training.py')\n",
    "\n",
    "# Create an experiment\n",
    "experiment = Experiment(workspace = ws, name = 'diabetes-training')\n",
    "\n",
    "# Run the experiment\n",
    "run = experiment.submit(config=estimator)\n",
    "# Show the run details while running\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the experiment runs more quickly because a matching environment has been cached from the previous run, so it doesn't need to be recreated on the local compute. However, even on a different compute target, the same environment would be created and used - ensuring consistency for your experiment script execution context.\n",
    "\n",
    "Let's look at the metrics and outputs from the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get logged metrics\n",
    "metrics = run.get_metrics()\n",
    "for key in metrics.keys():\n",
    "        print(key, metrics.get(key))\n",
    "print('\\n')\n",
    "for file in run.get_file_names():\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like this model is slightly better than the logistic regression model, so let's register it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Model\n",
    "\n",
    "run.register_model(model_path='outputs/diabetes_model.pkl', model_name='diabetes_model',\n",
    "                   tags={'Training context':'Estimator + Environment (Decision Tree)'}, properties={'AUC': run.get_metrics()['AUC'], 'Accuracy': run.get_metrics()['Accuracy']})\n",
    "\n",
    "for model in Model.list(ws):\n",
    "    print(model.name, 'version:', model.version)\n",
    "    for tag_name in model.tags:\n",
    "        tag = model.tags[tag_name]\n",
    "        print ('\\t',tag_name, ':', tag)\n",
    "    for prop_name in model.properties:\n",
    "        prop = model.properties[prop_name]\n",
    "        print ('\\t',prop_name, ':', prop)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View Registered Environments\n",
    "\n",
    "In addition to registering your own environments, you can leverage pre-built \"curated\" environments for common experiment types. The following code lists all registered environments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from azureml.core import Environment\n",
    "\n",
    "envs = Environment.list(workspace=ws)\n",
    "for env in envs:\n",
    "    print(\"Name\",env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All curated environments have names that begin ***AzureML-*** (you can't use this prefix for your own environments).\n",
    "\n",
    "Let's explore the curated environments in more depth and see what packages are included in each of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for env in envs:\n",
    "    if env.startswith(\"AzureML\"):\n",
    "        print(\"Name\",env)\n",
    "        print(\"packages\", envs[env].python.conda_dependencies.serialize_to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **More Information**: For more information about environments in Azure Machine Learning, see [the Azure Machine Learning documentation](https://docs.microsoft.com/azure/machine-learning/service/how-to-use-environments)"
   ]
  }
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