Get started with getML

In this example, you will learn about the basic concepts of getML. You will tackle a simple problem using the Python API in order to gain a technical understanding of the benefits of getML. More specifically, you will learn how to do the following:

1. Start a new project

2. Define a data model

3. Building a pipeline

4. Working with a pipeline

The guide is applicable to both the enterprise and the community editions of getML. The highlights of the two are mentioned under community vs enterprise edition section below.

You have not installed getML on your machine yet? Before you get started, head over to the installation instructions: for the enterprise edition or for the community edition.

Introduction

Automated machine learning (AutoML) has attracted a great deal of attention in recent years. The goal is to simplify the application of traditional machine learning methods to real world business problems by automating key steps of a data science project, such as feature extraction, model selection, and hyperparameter optimization. With AutoML, data scientists are able to develop and compare dozens of models, gain insights, generate predictions, and solve more business problems in less time.

While it is often claimed that AutoML covers the complete workflow of a data science project - from the raw data set to the deployable machine learning models - current solutions have one major drawback: They cannot handle real world business data. This data typically comes in the form relational data. The relevant information is scattered over a multitude of tables that are related via so-called join keys. In order to start an AutoML pipeline, a flat feature table has to be created from the raw relational data by hand. This step is called feature engineering and is a tedious and error-prone process that accounts for up to 90% of the time in a data science project.

getML adds automated feature engineering on relational data and time series to AutoML. The getML algorithms, Multirel and Relboost, find the right aggregations and subconditions needed to construct meaningful features from the raw relational data. This is done by performing a sophisticated, gradient-boosting-based heuristic. In doing so, getML brings the vision of end-to-end automation of machine learning within reach for the first time. Note that getML also includes automated model deployment via a HTTP endpoint or database connectors. This topic is covered in other material.

All functionality of getML is implemented in the so-called getML engine. It is written in C++ to achieve the highest performance and efficiency possible and is responsible for all the heavy lifting. The getML Python API acts as a bridge to communicate with engine. In addition, the getML monitor (available in enterprise edition) provides a Go-based graphical user interface to ease working with getML and significantly accelerate your workflow.

In this article, we start with a brief glimpse of different toolsets offered by getML community and enterprise editions. Later on, you will learn the basic steps and commands to tackle your data science projects using the Python API. For illustration purpose we will also touch how an example data set like the one used here would have been dealt with using classical data science tools. In contrast, we will show how the most tedious part of a data science project - merging and aggregating a relation data set - is automated using getML. At the end of this tutorial you are ready to tackle your own use cases with getML or dive deeper into our software using a variety of follow-up material.

Community vs enterprise edition

Before you start the tutorial, here are the highlights of the open-source getML community edition and full-featured getML enterprise edition:

	Community edition	Enterprise edition
License	Elastic Licence v2	Proprietary
Platform	Linux & Docker	Also macOS
Preprocessors	EmailDomain, Imputation, Seasonal, Substring, TextFieldSplitter	Also Mapping
Feature learners	FastProp	Also Multirel, Relboost, RelMT
Predictors	LinearRegression, LogisticRegression, XGBoost	Same
Productionization	Transpilation to human-readable SQL	Also transpilation to SQLite, Spark SQL, SAP HANA, BigQuery, SnowflakeSQL, TSQL. Built-in HTTP Endpoints.
Hyperparameter optimization	RandomSearch, LatinHypercube, GaussianOptimization, Customized tuning routines	Same
Database connectors	SQLite, MySQL, MariaDB, PostgreSQL	Also Greenplum, ODBC, SAP HANA, BigQuery, Snowflake
Other data sources	CSV, Parquet, Pandas, Arrow, Pyspark, JSON	Also S3
Other functionalities	Memory mapping	Also web frontend

Starting a new project

After you’ve successfully installed getML (enterprise or community), you can begin by executing the following in a jupyter-notebook:

>>> import getml
>>> print(f"getML API version: {getml.__version__}\n")
getML API version: 1.3.0

>>> getml.engine.launch()
Launched the getML engine. The log output will be stored in
/home/xxxxx/.getML/logs/xxxxxxxxxxxxxx.log.

This will import the getML Python API, launch the engine and the monitor.

Alternatively, you can also launch the getML engine and the monitor as follows:

• – On Mac, execute getml-cli inside a terminal or double-click the application icon.

• – On Windows/docker, execute run.sh in Git Bash.

• – On Linux, execute getML inside a terminal.

Now, inside Python, execute import getml to import the API.

The getML Monitor, available in the enterprise edition, is the frontend to the engine. It should open automatically by launching the engine. In case it does not, visit http://localhost:1709/ to open it. From now on, the entire analysis is run from Python. We will cover the getML monitor in a later tutorial but feel free to check what is going on while following this guide.

The entry-point for your project is the project module. From here, you can start projects and control running projects. Further, you have access to all project specific entities, and you can export a project as a .getml bundle to disk or load a .getml bundle from disk. To see the running projects, you can execute:

>>> getml.project
Cannot reach the getML engine. Please make sure you have set a project.
To set: `getml.engine.set_project`
Available projects:

This message tells us, that we have no running engine instance because we have not set a project. So, we follow the advice and create a new project. All data sets and models belonging to a project will be stored in ~/.getML/projects.

>>> getml.engine.set_project("getting_started")
Connected to project 'getting_started'
http://localhost:1709/#/listprojects/getting_started/

Now, when you check the current projects:

>>> getml.project
Current project:
getting_started

Data set

The data set used in this tutorial consists of 2 tables. The so-called population table represents the entities we want to make a prediction about in the analysis. The peripheral table contains additional information and is related to the population table via a join key. Such a data set could appear, for example, in a customer churn analysis where each row in the population table represents a customer and each row in the peripheral table represents a transaction. It could also be part of a predictive maintenance campaign where each row in the population table corresponds to a particular machine in a production line and each row in the peripheral table to a measurement from a certain sensor.

In this guide, however, we do not assume any particular use case. After all, getML is applicable to a wide range of problems from different domains. Use cases from specific fields are covered in other articles.

>>> population_table, peripheral_table = getml.datasets.make_numerical(
...     n_rows_population=500,
...     n_rows_peripheral=100000,
...     random_state=1709
... )

>>> getml.project.data_frames
    name                        rows     columns   memory usage
0   numerical_peripheral_1709   100000         3           2.00 MB
1   numerical_population_1709      500         4           0.01 MB

>>> population_table
Name   time_stamp                    join_key   targets   column_01
Role   time_stamp                    join_key    target   numerical
Units   time stamp, comparison only
    0   1970-01-01 00:00:00.470834           0       101     -0.6295
    1   1970-01-01 00:00:00.899782           1        88     -0.9622
    2   1970-01-01 00:00:00.085734           2        17      0.7326
    3   1970-01-01 00:00:00.365223           3        74     -0.4627
    4   1970-01-01 00:00:00.442957           4        96     -0.8374
        ...                                ...       ...     ...
  495   1970-01-01 00:00:00.945288         495        93      0.4998
  496   1970-01-01 00:00:00.518100         496       101     -0.4657
  497   1970-01-01 00:00:00.312872         497        59      0.9932
  498   1970-01-01 00:00:00.973845         498        92      0.1197
  499   1970-01-01 00:00:00.688690         499       101     -0.1274


  500 rows x 4 columns
  memory usage: 0.01 MB
  name: numerical_population_1709
  type: getml.data.DataFrame
  url: http://localhost:1709/#/getdataframe/getting_started/numerical_population_1709/

The population table contains 4 columns. The column called column_01 contains a random numerical value. The next column, targets, is the one we want to predict in the analysis. To this end, we also need to use the information from the peripheral table.

The relationship between the population and peripheral table is established using the join_key and time_stamp columns: Join keys are used to connect one or more rows from one table with one or more rows from the other table. Time stamps are used to limit these joins by enforcing causality and thus ensuring that no data from the future is used during the training.

In the peripheral table, columns_01 also contains a random numerical value. The population table and the peripheral table have a one-to-many relationship via join_key. This means that one row in the population table is associated to many rows in the peripheral table. In order to use the information from the peripheral table, we need to merge the many rows corresponding to one entry in the population table into so-called features. This is done using certain aggregations.

For example, such an aggregation could be the sum of all values in column_01. We could also apply a subcondition, like taking only values into account that fall into a certain time range with respect to the entry in the population table. In SQL code such a feature would look like this:

SELECT COUNT( * )
FROM POPULATION t1
LEFT JOIN PERIPHERAL t2
ON t1.join_key = t2.join_key
WHERE (
   ( t1.time_stamp - t2.time_stamp <= TIME_WINDOW )
) AND t2.time_stamp <= t1.time_stamp
GROUP BY t1.join_key,
     t1.time_stamp;

Unfortunately, neither the right aggregation nor the right subconditions are clear a priori. The feature that allows us to predict the target best could very well be e.g. the average of all values in column_01 that fall below a certain threshold, or something completely different. If you were to tackle this problem with classical machine learning tools, you would have to write many SQL features by hand and find the best ones in a trial-and-error-like fashion. At best, you could apply some domain knowledge that guides you towards the right direction. This approach, however, bears two major disadvantages that prevent you from finding the best-performing features.

1. You might not have sufficient domain knowledge.

2. You might not have sufficient resources for such a time-consuming, tedious, and error-prone process.

This is where getML comes in. It finds the correct features for you - automatically. You do not need to manually merge and aggregate tables in order to get started with a data science project. In addition, getML uses the derived features in a classical AutoML setting to easily make predictions with established and well-performing algorithms. This means getML provides an end-to-end solution starting from the relational data to a trained ML-model. How this is done via the getML Python API is demonstrated in the following.

Defining the data model

Most machine learning problems on relational data can be expressed as a simple star schema. This example is no exception, so we will use the predefined StarSchema class.

>>> split = getml.data.split.random(train=0.8, test=0.2)

>>> star_schema = getml.data.StarSchema(
...     population=population_table, alias="population", split=split)

>>> star_schema.join(peripheral_table,
...                  alias="peripheral",
...                  on="join_key",
...                  time_stamps="time_stamp",
... )

Building a pipeline

Now we can define the feature learner. Additionally, you can alter some hyperparameters like the number of features you want to train or the list of aggregations to select from when building features.

>>> fastprop = getml.feature_learning.FastProp(
...     num_features=10,
...     aggregation=[
...         getml.feature_learning.aggregations.Count,
...         getml.feature_learning.aggregations.Sum
...     ],
... )

getML bundles the sequential operations of a data science project (Preprocessing, Feature engineering and Predicting) into Pipeline objects. In addition to the Placeholders representing the DataFrames you also have to provide a feature learner (from getml.feature_learning) and a predictor (from getml.predictors).

>>> pipe = getml.pipeline.Pipeline(
...     data_model=star_schema.data_model,
...     feature_learners=[fastprop],
...     predictors=[getml.predictors.LinearRegression()],
... )

We have chosen a narrow search field in aggregation space by only letting FastProp use Count and Sum. For the sake of demonstration, we use a simple LinearRegression and construct only 10 different features. In real world projects you would construct at least ten times this number and get results significantly better than what we will achieve here.

Working with a pipeline

Now, that we have defined a Pipeline, we can let getML do the heavy lifting of your typical data science project. With a well-defined Pipeline, you can, i.a.:

• fit() the pipeline, to learn the logic behind your features (also referred to as training);

• score() the pipeline to evaluate its performance on unseen data;

• transform() the pipeline and materialize the learned logic into concrete (numerical) features;

• predict() the targets for unseen data;

• deploy() the pipeline to an http endpoint.

Training

When fitting the model, we pass the handlers to the actual data residing in the getML engine – the DataFrames.

>>> pipe.fit(star_schema.train)
Checking data model...

Staging...
[========================================] 100%

Checking...
[========================================] 100%


OK.

Staging...
[========================================] 100%

FastProp: Training 5 features...
[========================================] 100%

FastProp: Building features...
[========================================] 100%

LinearRegression: Training as predictor...
[========================================] 100%

Trained pipeline.
Time taken: 0h:0m:0.049154

Pipeline(data_model='population',
        feature_learners=['FastProp'],
        feature_selectors=[],
        include_categorical=False,
        loss_function='SquareLoss',
        peripheral=['peripheral'],
        predictors=['LinearRegression'],
        preprocessors=[],
        share_selected_features=0.5,
        tags=['container-s0mKB6'])

url: http://localhost:1709/#/getpipeline/getting_started/MXzNDT/0/

That’s it. The features learned by FastProp as well as the LinearRegression in are now trained on our data set.

Scoring

We can also score our algorithms on the test set.

>>> pipe.score(star_schema.test)

Staging...
[========================================] 100%

Preprocessing...
[========================================] 100%

FastProp: Building features...
[========================================] 100%

    date time             set used    target         mae      rmse   rsquared
0   2022-09-02 10:14:12   train       targets     3.3721    4.1891     0.9853
1   2022-09-02 10:14:12   test        targets     3.7548    4.7093     0.981

Our model is able to predict the target variable in the newly generated data set pretty accurately. Though, the enterprise feature learner Multirel performs even better here with R² of 0.9995 and MAE and RMSE of 0.07079 and 0.1638 respectively.

Making predictions

Let’s simulate the arrival of unseen data and generate another population table. Since the data model is already stored in the pipeline, we do not need to recreate it and can just use a Container instead of a StarSchema.

>>> population_table_unseen, peripheral_table_unseen = getml.datasets.make_numerical(
...     n_rows_population=200,
...     n_rows_peripheral=8000,
...     random_state=1711,
... )

>>> container_unseen = getml.data.Container(population_table_unseen)

>>> container_unseen.add(peripheral=peripheral_table_unseen)

>>> yhat = pipe.predict(container_unseen.full)

Staging...
[========================================] 100%

Preprocessing...
[========================================] 100%

FastProp: Building features...
[========================================] 100%

>>> print(yhat[:10])
[[ 4.16876676]
 [17.32933   ]
 [26.62467516]
 [-5.30655759]
 [27.4984785 ]
 [21.48631811]
 [18.16896219]
 [ 5.2784719 ]
 [20.5992354 ]
 [26.20538556]]

Extracting features

Of course you can also transform a specific data set into the corresponding features in order to insert them into another machine learning algorithm.

>>> features = pipe.transform(container_unseen.full)

>>> print(features)
[[-7.14232213e-01  2.39745475e-01  2.62855261e-01  1.28462060e-02
   5.00000000e+00 -3.18568319e-01]
 [-1.17601634e-01  3.42472663e+00  3.61423201e+00  3.24305583e-02
   1.40000000e+01  3.94656676e-01]
 [-2.48645436e+00  1.27495266e+01  1.33228011e+01  1.99520872e-02
   3.60000000e+01  1.24700392e-01]
 ...
 [ 9.55124379e-01  9.16437833e-01  9.40897830e-01  2.73040074e-02
   8.00000000e+00 -7.49963688e-01]
 [-3.56023429e+00  3.37346772e+00  2.11562428e+00  2.53698895e-02
   1.50000000e+01 -7.27880243e-01]
 [ 2.72804029e-02  2.87302783e-02  5.36035230e-02  2.77103542e-02
   2.00000000e+00 -3.53700424e-01]]

If you want to see a SQL transpilation of a feature’s logic, you can do so by clicking on the feature in the monitor (enterprise edition only) or by inspecting the sql attribute on a feature. A Pipelines features are hold by the Features container. For example, to inspect the sql code of one of the features:

>>> pipe.features[1].sql

That should return something like this:

DROP TABLE IF EXISTS "FEATURE_1_5";

CREATE TABLE "FEATURE_1_5" AS
SELECT COUNT( * ) AS "feature_1_5",
      t1.rowid AS rownum
FROM "POPULATION__STAGING_TABLE_1" t1
INNER JOIN "PERIPHERAL__STAGING_TABLE_2" t2
ON t1."join_key" = t2."join_key"
WHERE t2."time_stamp" <= t1."time_stamp"
GROUP BY t1.rowid;

This very much resembles the ad hoc definition we tried in the beginning. The correct aggregation to use on this data set is Count. getML extracted this definition completely autonomously.

Next steps

This guide has shown you the very basics of getML. Starting with raw data you have completed a full project including feature engineering and linear regression using an automated end-to-end pipeline. The most tedious part of this process - finding the right aggregations and subconditions to construct a feature table from the relational data model - was also included in this pipeline.

But there’s more! Related articles show application of getML on real world data sets.

Also, don’t hesitate to contact us with your feedback.