Tutorial (part 2): Use automated machine learning to build your regression model

This tutorial is part two of a two-part tutorial series. In the previous tutorial, you prepared the NYC taxi data for regression modeling.

Now, you're ready to start building your model with Azure Machine Learning service. In this part of the tutorial, you will use the prepared data and automatically generate a regression model to predict taxi fare prices. Using the automated ML capabilities of the service, you define your machine learning goals and constraints, launch the automated machine learning process and then allow the algorithm selection and hyperparameter-tuning to happen for you. The automated ML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.

flow diagram

In this tutorial, you learn how to:

  • Setup a Python environment and import the SDK packages
  • Configure an Azure Machine Learning service workspace
  • Auto-train a regression model
  • Run the model locally with custom parameters
  • Explore the results
  • Register the best model

If you don’t have an Azure subscription, create a free account before you begin.

Note

Code in this article was tested with Azure Machine Learning SDK version 1.0.0

Prerequisites

  • Run the data preparation tutorial.
  • Automated machine learning configured environment e.g. Azure notebooks, Local Python environment or Data Science Virtual Machine. Setup automated machine learning.

Get the notebook

For your convenience, this tutorial is available as a Jupyter notebook. Run the regression-part2-automated-ml.ipynb notebook either in Azure Notebooks or in your own Jupyter notebook server.

Learn how to run notebooks by following the article, Use Jupyter notebooks to explore this service.

Import packages

Import Python packages you need in this tutorial.

import azureml.core
import pandas as pd
from azureml.core.workspace import Workspace
from azureml.train.automl.run import AutoMLRun
import time
import logging
import os

Configure workspace

Create a workspace object from the existing workspace. A Workspace is a class that accepts your Azure subscription and resource information, and creates a cloud resource to monitor and track your model runs. Workspace.from_config() reads the file aml_config/config.json and loads the details into an object named ws. ws is used throughout the rest of the code in this tutorial.

Once you have a workspace object, specify a name for the experiment and create and register a local directory with the workspace. The history of all runs is recorded under the specified experiment and in Azure portal.

ws = Workspace.from_config()
# choose a name for the run history container in the workspace
experiment_name = 'automated-ml-regression'
# project folder
project_folder = './automated-ml-regression'

output = {}
output['SDK version'] = azureml.core.VERSION
output['Subscription ID'] = ws.subscription_id
output['Workspace'] = ws.name
output['Resource Group'] = ws.resource_group
output['Location'] = ws.location
output['Project Directory'] = project_folder
pd.set_option('display.max_colwidth', -1)
pd.DataFrame(data=output, index=['']).T

Explore data

Utilize the data flow object created in the previous tutorial. Open and execute the data flow and review the results.

import azureml.dataprep as dprep

file_path = os.path.join(os.getcwd(), "dflows.dprep")

package_saved = dprep.Package.open(file_path)
dflow_prepared = package_saved.dataflows[0]
dflow_prepared.get_profile()
Type Min Max Count Missing Count Not Missing Count Percent missing Error Count Empty count 0.1% Quantile 1% Quantile 5% Quantile 25% Quantile 50% Quantile 75% Quantile 95% Quantile 99% Quantile 99.9% Quantile Mean Standard Deviation Variance Skewness Kurtosis
vendor FieldType.STRING 1 VTS 6148.0 0.0 6148.0 0.0 0.0 0.0
pickup_weekday FieldType.STRING Friday Wednesday 6148.0 0.0 6148.0 0.0 0.0 0.0
pickup_hour FieldType.DECIMAL 0 23 6148.0 0.0 6148.0 0.0 0.0 0.0 0 2.90047 2.69355 9.72889 16 19.3713 22.6974 23 23 14.2731 6.59242 43.46 -0.693723 -0.570403
pickup_minute FieldType.DECIMAL 0 59 6148.0 0.0 6148.0 0.0 0.0 0.0 0 4.99701 4.95833 14.1528 29.3832 44.6825 56.4444 58.9909 59 29.427 17.4333 303.921 0.0120999 -1.20981
pickup_second FieldType.DECIMAL 0 59 6148.0 0.0 6148.0 0.0 0.0 0.0 0 5.28131 5 14.7832 29.9293 44.725 56.7573 59 59 29.7443 17.3595 301.351 -0.0252399 -1.19616
dropoff_weekday FieldType.STRING Friday Wednesday 6148.0 0.0 6148.0 0.0 0.0 0.0
dropoff_hour FieldType.DECIMAL 0 23 6148.0 0.0 6148.0 0.0 0.0 0.0 0 2.57153 2 9.58795 15.9994 19.6184 22.8317 23 23 14.2105 6.71093 45.0365 -0.687292 -0.61951
dropoff_minute FieldType.DECIMAL 0 59 6148.0 0.0 6148.0 0.0 0.0 0.0 0 5.44383 4.84694 14.1036 28.8365 44.3102 56.6892 59 59 29.2907 17.4108 303.136 0.0222514 -1.2181
dropoff_second FieldType.DECIMAL 0 59 6148.0 0.0 6148.0 0.0 0.0 0.0 0 5.07801 5 14.5751 29.5972 45.4649 56.2729 59 59 29.772 17.5337 307.429 -0.0212575 -1.226
store_forward FieldType.STRING N Y 6148.0 0.0 6148.0 0.0 0.0 0.0
pickup_longitude FieldType.DECIMAL -74.0781 -73.7459 6148.0 0.0 6148.0 0.0 0.0 0.0 -74.0578 -73.9639 -73.9656 -73.9508 -73.9255 -73.8529 -73.8302 -73.8238 -73.7697 -73.9123 0.0503757 0.00253771 0.352172 -0.923743
pickup_latitude FieldType.DECIMAL 40.5755 40.8799 6148.0 0.0 6148.0 0.0 0.0 0.0 40.632 40.7117 40.7115 40.7213 40.7565 40.8058 40.8478 40.8676 40.8778 40.7649 0.0494674 0.00244702 0.205972 -0.777945
dropoff_longitude FieldType.DECIMAL -74.0857 -73.7209 6148.0 0.0 6148.0 0.0 0.0 0.0 -74.0775 -73.9875 -73.9882 -73.9638 -73.935 -73.8755 -73.8125 -73.7759 -73.7327 -73.9202 0.0584627 0.00341789 0.623622 -0.262603
dropoff_latitude FieldType.DECIMAL 40.5835 40.8797 6148.0 0.0 6148.0 0.0 0.0 0.0 40.5973 40.6928 40.6911 40.7226 40.7567 40.7918 40.8495 40.868 40.8787 40.7583 0.0517399 0.00267701 0.0390404 -0.203525
passengers FieldType.DECIMAL 1 6 6148.0 0.0 6148.0 0.0 0.0 0.0 1 1 1 1 1 5 5 6 6 2.39249 1.83197 3.3561 0.763144 -1.23467
distance FieldType.DECIMAL 0.01 32.34 6148.0 0.0 6148.0 0.0 0.0 0.0 0.0108744 0.743898 0.738194 1.243 2.40168 4.74478 10.5136 14.9011 21.8035 3.5447 3.2943 10.8524 1.91556 4.99898
cost FieldType.DECIMAL 0.1 88 6148.0 0.0 6148.0 0.0 0.0 0.0 2.33837 5.00491 5 6.93129 10.524 17.4811 33.2343 50.0093 63.1753 13.6843 9.66571 93.426 1.78518 4.13972

You prepare the data for the experiment by adding columns to dflow_x to be features for our model creation. You define dflow_y to be our prediction value; cost.

dflow_X = dflow_prepared.keep_columns(['pickup_weekday','pickup_hour', 'distance','passengers', 'vendor'])
dflow_y = dflow_prepared.keep_columns('cost')

Split data into train and test sets

Now you split the data into training and test sets using the train_test_split function in the sklearn library. This function segregates the data into the x (features) data set for model training and the y (values to predict) data set for testing. The test_size parameter determines the percentage of data to allocate to testing. The random_state parameter sets a seed to the random generator, so that your train-test splits are always deterministic.

from sklearn.model_selection import train_test_split

x_df = dflow_X.to_pandas_dataframe()
y_df = dflow_y.to_pandas_dataframe()

x_train, x_test, y_train, y_test = train_test_split(x_df, y_df, test_size=0.2, random_state=123)
# flatten y_train to 1d array
y_train.values.flatten()

You now have the necessary packages and data ready for auto training for your model.

Automatically train a model

To automatically train a model:

  1. Define settings for the experiment run
  2. Submit the experiment for model tuning

Define settings for autogeneration and tuning

Define the experiment parameters and models settings for autogeneration and tuning. View the full list of settings.

Property Value in this tutorial Description
iteration_timeout_minutes 10 Time limit in minutes for each iteration
iterations 30 Number of iterations. In each iteration, the model trains with the data with a specific pipeline
primary_metric spearman_correlation Metric that you want to optimize.
preprocess True True enables experiment to perform preprocessing on the input.
verbosity logging.INFO Controls the level of logging.
n_cross_validationss 5 Number of cross validation splits
automl_settings = {
    "iteration_timeout_minutes" : 10,
    "iterations" : 30,
    "primary_metric" : 'spearman_correlation',
    "preprocess" : True,
    "verbosity" : logging.INFO,
    "n_cross_validations": 5
}
from azureml.train.automl import AutoMLConfig

# local compute
automated_ml_config = AutoMLConfig(task = 'regression',
                             debug_log = 'automated_ml_errors.log',
                             path = project_folder,
                             X = x_train.values,
                             y = y_train.values.flatten(),
                             **automl_settings)

Train the automatic regression model

Start the experiment to run locally. Pass the defined automated_ml_config object to the experiment, and set the output to True to view progress during the experiment.

from azureml.core.experiment import Experiment
experiment=Experiment(ws, experiment_name)
local_run = experiment.submit(automated_ml_config, show_output=True)
Parent Run ID: AutoML_02778de3-3696-46e9-a71b-521c8fca0651
*******************************************************************************************
ITERATION: The iteration being evaluated.
PIPELINE: A summary description of the pipeline being evaluated.
DURATION: Time taken for the current iteration.
METRIC: The result of computing score on the fitted pipeline.
BEST: The best observed score thus far.
*******************************************************************************************

 ITERATION   PIPELINE                                       DURATION      METRIC      BEST
         0   MaxAbsScaler ExtremeRandomTrees                0:00:08       0.9447    0.9447
         1   StandardScalerWrapper GradientBoosting         0:00:09       0.9536    0.9536
         2   StandardScalerWrapper ExtremeRandomTrees       0:00:09       0.8580    0.9536
         3   StandardScalerWrapper RandomForest             0:00:08       0.9147    0.9536
         4   StandardScalerWrapper ExtremeRandomTrees       0:00:45       0.9398    0.9536
         5   MaxAbsScaler LightGBM                          0:00:08       0.9562    0.9562
         6   StandardScalerWrapper ExtremeRandomTrees       0:00:27       0.8282    0.9562
         7   StandardScalerWrapper LightGBM                 0:00:07       0.9421    0.9562
         8   MaxAbsScaler DecisionTree                      0:00:08       0.9526    0.9562
         9   MaxAbsScaler RandomForest                      0:00:09       0.9355    0.9562
        10   MaxAbsScaler SGD                               0:00:09       0.9602    0.9602
        11   MaxAbsScaler LightGBM                          0:00:09       0.9553    0.9602
        12   MaxAbsScaler DecisionTree                      0:00:07       0.9484    0.9602
        13   MaxAbsScaler LightGBM                          0:00:08       0.9540    0.9602
        14   MaxAbsScaler RandomForest                      0:00:10       0.9365    0.9602
        15   MaxAbsScaler SGD                               0:00:09       0.9602    0.9602
        16   StandardScalerWrapper ExtremeRandomTrees       0:00:49       0.9171    0.9602
        17   SparseNormalizer LightGBM                      0:00:08       0.9191    0.9602
        18   MaxAbsScaler DecisionTree                      0:00:08       0.9402    0.9602
        19   StandardScalerWrapper ElasticNet               0:00:08       0.9603    0.9603
        20   MaxAbsScaler DecisionTree                      0:00:08       0.9513    0.9603
        21   MaxAbsScaler SGD                               0:00:08       0.9603    0.9603
        22   MaxAbsScaler SGD                               0:00:10       0.9602    0.9603
        23   StandardScalerWrapper ElasticNet               0:00:09       0.9603    0.9603
        24   StandardScalerWrapper ElasticNet               0:00:09       0.9603    0.9603
        25   MaxAbsScaler SGD                               0:00:09       0.9603    0.9603
        26   TruncatedSVDWrapper ElasticNet                 0:00:09       0.9602    0.9603
        27   MaxAbsScaler SGD                               0:00:12       0.9413    0.9603
        28   StandardScalerWrapper ElasticNet               0:00:07       0.9603    0.9603
        29    Ensemble                                      0:00:38       0.9622    0.9622

Explore the results

Explore the results of automatic training with a Jupyter widget or by examining the experiment history.

Option 1: Add a Jupyter widget to see results

If you are using a Juypter notebook, use this Jupyter notebook widget to see a graph and a table of all results.

from azureml.widgets import RunDetails
RunDetails(local_run).show()

Jupyter Widget run details Jupyter Widget plot

Option 2: Get and examine all run iterations in Python

Alternatively, you can retrieve the history of each experiment and explore the individual metrics for each iteration run.

children = list(local_run.get_children())
metricslist = {}
for run in children:
    properties = run.get_properties()
    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}
    metricslist[int(properties['iteration'])] = metrics

import pandas as pd
rundata = pd.DataFrame(metricslist).sort_index(1)
rundata
0 1 2 3 4 5 6 7 8 9 ... 20 21 22 23 24 25 26 27 28 29
explained_variance 0.811037 0.880553 0.398582 0.776040 0.663869 0.875911 0.115632 0.586905 0.851911 0.793964 ... 0.850023 0.883603 0.883704 0.880797 0.881564 0.883708 0.881826 0.585377 0.883123 0.886817
mean_absolute_error 2.189444 1.500412 5.480531 2.626316 2.973026 1.550199 6.383868 4.414241 1.743328 2.294601 ... 1.797402 1.415815 1.418167 1.578617 1.559427 1.413042 1.551698 4.069196 1.505795 1.430957
median_absolute_error 1.438417 0.850899 4.579662 1.765210 1.594600 0.869883 4.266450 3.627355 0.954992 1.361014 ... 0.973634 0.774814 0.797269 1.147234 1.116424 0.783958 1.098464 2.709027 1.003728 0.851724
normalized_mean_absolute_error 0.024908 0.017070 0.062350 0.029878 0.033823 0.017636 0.072626 0.050219 0.019833 0.026105 ... 0.020448 0.016107 0.016134 0.017959 0.017741 0.016076 0.017653 0.046293 0.017131 0.016279
normalized_median_absolute_error 0.016364 0.009680 0.052101 0.020082 0.018141 0.009896 0.048538 0.041267 0.010865 0.015484 ... 0.011077 0.008815 0.009070 0.013052 0.012701 0.008919 0.012497 0.030819 0.011419 0.009690
normalized_root_mean_squared_error 0.047968 0.037882 0.085572 0.052282 0.065809 0.038664 0.109401 0.071104 0.042294 0.049967 ... 0.042565 0.037685 0.037557 0.037643 0.037513 0.037560 0.037465 0.072077 0.037249 0.036716
normalized_root_mean_squared_log_error 0.055353 0.045000 0.110219 0.065633 0.063589 0.044412 0.123433 0.092312 0.046130 0.055243 ... 0.046540 0.041804 0.041771 0.045175 0.044628 0.041617 0.044405 0.079651 0.042799 0.041530
r2_score 0.810900 0.880328 0.398076 0.775957 0.642812 0.875719 0.021603 0.586514 0.851767 0.793671 ... 0.849809 0.880142 0.880952 0.880586 0.881347 0.880887 0.881613 0.548121 0.882883 0.886321
root_mean_squared_error 4.216362 3.329810 7.521765 4.595604 5.784601 3.398540 9.616354 6.250011 3.717661 4.392072 ... 3.741447 3.312533 3.301242 3.308795 3.297389 3.301485 3.293182 6.335581 3.274209 3.227365
root_mean_squared_log_error 0.243184 0.197702 0.484227 0.288349 0.279367 0.195116 0.542281 0.405559 0.202666 0.242702 ... 0.204464 0.183658 0.183514 0.198468 0.196067 0.182836 0.195087 0.349935 0.188031 0.182455
spearman_correlation 0.944743 0.953618 0.857965 0.914703 0.939846 0.956159 0.828187 0.942069 0.952581 0.935477 ... 0.951287 0.960335 0.960195 0.960279 0.960288 0.960323 0.960161 0.941254 0.960293 0.962158
spearman_correlation_max 0.944743 0.953618 0.953618 0.953618 0.953618 0.956159 0.956159 0.956159 0.956159 0.956159 ... 0.960303 0.960335 0.960335 0.960335 0.960335 0.960335 0.960335 0.960335 0.960335 0.962158

12 rows × 30 columns

Retrieve the best model

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.

best_run, fitted_model = local_run.get_output()
print(best_run)
print(fitted_model)

Register the model

Register the model in your Azure Machine Learning service workspace.

description = 'Automated Machine Learning Model'
tags = None
local_run.register_model(description=description, tags=tags)
local_run.model_id # Use this id to deploy the model as a web service in Azure

Test the best model accuracy

Use the best model to run predictions on the test data set. The function predict uses the best model, and predicts the values of y (trip cost) from the x_test data set. Print the first 10 predicted cost values from y_predict.

y_predict = fitted_model.predict(x_test.values)
print(y_predict[:10])

Compare the predicted cost values with the actual cost values. Use the y_test dataframe, and convert it to a list to compare to the predicted values. The function mean_squared_error takes two arrays of values, and calculates the average squared error between them. Taking the square root of the result gives an error in the same units as the y variable (cost), and indicates roughly how far your predictions are from the actual value.

from sklearn.metrics import mean_squared_error
from math import sqrt

y_actual = y_test.values.flatten().tolist()
rmse = sqrt(mean_squared_error(y_actual, y_predict))
rmse
4.0317375193408544

Run the following code to calculate MAPE (mean absolute percent error) using the full y_actual and y_predict data sets. This metric calculates an absolute difference between each predicted and actual value, sums all the differences, and then expresses that sum as a percent of the total of the actual values.

sum_actuals = sum_errors = 0

for actual_val, predict_val in zip(y_actual, y_predict):
    abs_error = actual_val - predict_val
    if abs_error < 0:
        abs_error = abs_error * -1

    sum_errors = sum_errors + abs_error
    sum_actuals = sum_actuals + actual_val

mean_abs_percent_error = sum_errors / sum_actuals
print("Model MAPE:")
print(mean_abs_percent_error)
print()
print("Model Accuracy:")
print(1 - mean_abs_percent_error)
Model MAPE:
0.11334441225861108

Model Accuracy:
0.8866555877413889

Clean up resources

Important

The resources you created can be used as prerequisites to other Azure Machine Learning service tutorials and how-to articles.

If you don't plan to use the resources you created here, delete them so you don't incur any charges.

  1. In the Azure portal, select Resource groups on the far left.

    Delete in Azure portal

  2. From the list, select the resource group you created.

  3. Select Delete resource group.

  4. Enter the resource group name, and then select Delete.

Next steps

In this automated machine learning tutorial, you:

  • Configured a workspace and prepared data for an experiment
  • Trained using an automated regression model locally with custom parameters
  • Explored and reviewed training results
  • Registered the best model

Deploy your model with Azure Machine Learning.