This short blog post gets you started with getML. You will learn the basic steps and commands to tackle your data science project using its Python API. The underlying Python script can be accessed here.
To make our short introduction as realistic as possible, we will start from a public-domain real-world data set. Let's pretend we are a company selling and shipping a large portfolio of different products. To both improve user experience and increase revenue, we want to offer a special wrapping in case a product was bought as a gift. But how to inform the costumers in question about our new service without annoying everyone else?
The most prolific approach to solve such kind of problems is Machine Learning. We will use it on the consumer expenditure public-use microdata provided by the U.S. Bureau of Labor Statistics to predict whether a product is bought as a gift or not.
But first of all you have to install getML. Just go to our download page, choose the track that fits you most, and unpack the tarball we provide. That's it.
To run the application, all you need to do is enter the unpacked
tarball and execute the
This starts up the getML engine, which was written in C++ for efficiency and takes care of all the heavy lifting, and the getML monitor, which serves as a convenient user interface.
Next, you need to log into the engine. Open the up the web browser
of your choice and enter
localhost:1709 in the address bar. You will
access a local HTTP server run by the monitor, which will ask you to
enter your credentials or to create a new account. Please note that
this account is not a local one but the one you set up via our
homepage. Thus, you also need a working internet connection to run the
Bundled with the getML binary you can also find its Python3 API. To install it, use the following commands
cd python python3 setup.py install
Finally, we need some data to work with. You have two options here.
With the preprocessing already done we will start by setting a new project in the getML engine and loading the prepared tables into the Python environment.
import pandas as pd import getml.engine engine.set_project("CE") CE_population_training = pd.read_csv("CE_population_training.csv") CE_population_validation = pd.read_csv("CE_population_validation.csv") CE_peripheral = pd.read_csv("CE_peripheral.csv")
In order for the automated feature engineering to get the most out of the data, we have to provided some additional information about its content. If a column contains e.g. the type of a product encoded in integers, operations like comparisons, summation, or the extraction the maximum would most probably make no sense. It, therefore, needs to be of recognized as categorical instead of discrete.
CATEGORICAL = [ "UCC", "UCC1", "UCC2", "UCC3", "UCC4", "UCC5"] DISCRETE = ["EXPNYR"] JOIN_KEYS = [ "NEWID", "BASKETID"] NUMERICAL = ["COST"] TARGETS = ["TARGET"] TIME_STAMPS = [ "TIME_STAMP", "TIME_STAMP_SHIFTED"]
units = dict() units["UCC"] = "UCC" units["UCC1"] = "UCC1" units["UCC2"] = "UCC2" units["UCC3"] = "UCC3" units["UCC4"] = "UCC4" units["UCC5"] = "UCC5" units["EXPNYR"] = "year, comparison only"
With all that additional information in place we can finally construct
DataFrames, which will serve as our handles for the tables
stored in the engine. Using the
.send() method we upload the
provided data to the engine and
.save() ensures the
df_population_training = engine.DataFrame( "POPULATION_TRAINING", join_keys=JOIN_KEYS, time_stamps=TIME_STAMPS, categorical=CATEGORICAL, discrete=DISCRETE, numerical=NUMERICAL, targets=TARGETS, units=units ).send(CE_population_training) df_population_training.save() df_population_validation = engine.DataFrame( "POPULATION_VALIDATION", join_keys=JOIN_KEYS, time_stamps=TIME_STAMPS, categorical=CATEGORICAL, discrete=DISCRETE, numerical=NUMERICAL, targets=TARGETS, units=units ).send(CE_population_validation) df_population_validation.save() df_peripheral = engine.DataFrame( "PERIPHERAL", join_keys=JOIN_KEYS, time_stamps=TIME_STAMPS, categorical=CATEGORICAL, discrete=DISCRETE, numerical=NUMERICAL, targets=TARGETS, units=units ).send(CE_peripheral) df_peripheral.save()
Now, all data is uploaded into the getML engine. But to train a model using these tables, we still need a way to represent their relations to each other.
We will do so with the concept of placeholders popularized by
Tensorflow and linking them using specific columns present in both
tables by calling the
import getml.models as models CE_placeholder = models.Placeholder("PERIPHERAL") CE_placeholder2 = models.Placeholder("PERIPHERAL") CE_placeholder.join( CE_placeholder2, join_key="NEWID", time_stamp="TIME_STAMP", other_time_stamp="TIME_STAMP_SHIFTED" ) CE_placeholder.join( CE_placeholder2, join_key="BASKETID", time_stamp="TIME_STAMP" )
For more information about this steps please have a look at detailed description.
Apart from our sophisticated algorithm for automated feature engineering in relational data, getML has two other main components.
The first one is the feature selector, which picks the best set of features from the generated ones. The second is the predictor, which is trained on the features to make predictions and is the component you already know from various other machine learning applications and libraries.
For both instances we will use a XGBoost classifier.
import getml.predictors as predictors feature_selector = predictors.XGBoostClassifier( booster="gbtree", n_estimators=100, n_jobs=6, max_depth=7, reg_lambda=500 ) predictor = predictors.XGBoostClassifier( booster="gbtree", n_estimators=100, n_jobs=6, max_depth=7, reg_lambda=500 )
Finally, we have all pieces together to construct the overall
model. For details about its arguments, please have a look into the
documentation. Like a
DataFrame a model needs
to be uploaded to the getML engine using the
.send() method too.
import getml.aggregations as aggregations import getml.loss_functions as loss_functions model = models.AutoSQLModel( population=CE_placeholder, peripheral=[CE_placeholder], predictor=predictor, loss_function=loss_functions.CrossEntropyLoss(), aggregation=[ aggregations.Avg, aggregations.Count, aggregations.CountDistinct, aggregations.CountMinusCountDistinct, aggregations.Max, aggregations.Median, aggregations.Min, aggregations.Sum ], use_timestamps=True, num_features=70, max_length=7, min_num_samples=100, shrinkage=0.1, grid_factor=1.0, regularization=0.0, round_robin=False, share_aggregations=0.04, share_conditions=0.8, sampling_factor=1.0 ).send()
To build the features and train the predictor, all you need to do is
to call the
.fit() method of the model.
model = model.fit( population_table=df_population_training, peripheral_tables=[df_peripheral] )
To see how well it performs, let's evaluate it on the validation set
scores = model.score( population_table=df_population_validation, peripheral_tables=[df_peripheral] )
Right now, getML supports six different scores: accuracy, AUC
(area under the ROC curve), and cross entropy for classification tasks
and MAE, RMSE, and R-squared (squared correlation coefficient) for
regression. Since determining whether a product was bought as a
present is a classification problem, we will recommend the AUC to
measure the performance of our model. If you wish, you can gather
additional data or tweak the parameters of the
improve it even further.
As soon as you are satisfied with the performance of your model you
can use it in production to make predictions on new and unseen data
ZEISS is working on delivering highly reliable machines and support processes. GetML allows building new predictive maintenance applications in a fraction of the time.
Predict item purchases that will be gifts. This analysis is based on a public domain data set provided by the American Bureau of Labor Statistics.