Bender 🤖

A Python package for faster, safer, and simpler ML processes.

Why use bender?

Bender will make your machine learning processes, faster, safer, simpler while at the same time making it easy and flexible. This is done by providing a set base component, around the core processes that will take place in a ML pipeline process. While also helping you with type hints about what your next move could be.

Pipeline Safety

The whole pipeline is build using generics from Python’s typing system. Resulting in an improved developer experience, as the compiler can know if your pipeline’s logic makes sense before it has started.

Load a data set

Bender makes most of the sklearn datasets available through the DataImporters.data_set(...) importer. Here will you need to pass an enum to define which dataset you want. It is also possible to load the data from sql, append different data sources and cache, and it is as simple as:

# Predifined data set
DataImporters.data_set(DataSets.IRIS)

# Load SQL
DataImporters.sql("url", "SELECT ...")

# Cache a sql import
DataImporters.sql("url", "SELECT ...")
    .cached("path/to/cache")
    .append(
        # Add more data from a different source (with same features)
        DataImporters.sql(...)
    )

Processing

When the data has been loaded is usually the next set to process the data in some way. bender will therefore provide different components that transforms features. Therefore making it easier to keep your logic consistent over multiple projects.

DataImporters.data_set(DataSets.IRIS)
    .process([
        # pl exp = e^(petal length)
        Transformations.exp_shift('petal length (cm)', output='pl exp'),

        # purchases = mean value of the json price values
        Transformations.unpack_json("purchases", key="price", output_feature="price", policy=UnpackPolicy.median_number()),

        ...
    ])

EDA

For view how the data is distribuated, is it also possible to explore the data.

await (DataImporters.data_set(DataSets.IRIS)
    .process([...])
    .explore([
        # Display all features in a hist
        Explorers.histogram(target='target'),

        # Display corr matrix and logs which features you could remove
        Explorers.correlation(input_features),

        # View how features relate in 2D
        Explorers.pair_plot('target'),
    ])

Splitting into train and test sets

There are many ways we can train and test, it is therefore easy to choose and switch between how it is done with bender.

await (DataImporters.data_set(DataSets.IRIS)
    .process([...])

    # Have 70% as train and 30 as test
    .split(SplitStrategies.ratio(0.7))

    # Have 70% of each target group in train and the rest in test
    .split(SplitStrategies.uniform_ratio("target", 0.7))

    # Sorts by the key and taks the first 70% as train
    .split(SplitStrategies.sorted_ratio("target", 0.7))

Training

After you have split the data set into train and test, then you can train with the following.

await (DataImporters.data_set(DataSets.IRIS)
    .split(...)
    .train(
        # train kneighbours on the train test
        Trainers.kneighbours(),
        input_features=[...],
        target_feature="target"
    )

Evaluate

After you have a model will it be smart to test how well it works.

await (DataImporters.data_set(DataSets.IRIS)
    .split(...)
    .train(...)
    .evaluate([
        # Only present the confusion matrix
        Evaluators.confusion_matrix(),
        Evaluators.roc_curve(),
        Evaluators.precision_recall(),
    ])

Save model

At last would you need to store the model. You can therefore select one of manny exporters.

await (DataImporters.data_set(DataSets.IRIS)
    .split(...)
    .train(...)
    .export_model(Exporters.aws_s3(...))

Examples

An example of the IRIS data set which trains a model to perfection

await (DataImporters.data_set(DataSets.IRIS)
    .process([
        Transformations.exp_shift('petal length (cm)', output='pl exp'),
        Transformations.exp_shift('petal width (cm)', output='pw exp'),
    ])
    .explore([
        Explorers.histogram(target='target'),
        Explorers.correlation(input_features),
        Explorers.pair_plot('target'),
    ])
    .split(SplitStrategies.uniform_ratio("target", 0.7))
    .train(Trainers.kneighbours(), input_features=input_features, target_feature="target")
    .evaluate([
        Evaluators.confusion_matrix()
    ])
    .metric(Metrics.log_loss())
    .run())

XGBoost Example

Below is a simple example for training a XGBoosted tree

DataImporters.sql(sql_url, sql_query)

    .process([ # Preproces the data
        # Extract advanced information from json data
        Transformations.unpack_json("purchases", key="price", output_feature="price", policy=UnpackPolicy.median_number())

        Transformations.log_normal_shift("y_values", "y_log"),

        # Get date values from a date feature
        Transformations.date_component("month", "date", output_feature="month_value"),
    ])
    .split(SplitStrategies.ratio(0.7))

    # Train a XGBoosted Tree model
    .train(
        Trainers.xgboost(),
        input_features=['y_log', 'price', 'month_value', 'country', ...],
        target_feature='did_buy_product_x'
    )
    .evaluate([
        Evaluators.roc_curve(),
        Evaluators.confusion_matrix(),
        Evaluators.precision_recall(
            # Overwrite where to export the evaluated result
            Exporter.disk("precision-recall.png")
        ),
    ])

Predicting Example

Below will a model be loaded from a AWS S3 bucket, preprocess the data, and predict the output.
This will also make sure that the features are valid before predicting.

ModelLoaders
    # Fetch Model
    .aws_s3("path/to/model", s3_config)

    # Load data
    .import_data(
        DataImporters.sql(sql_url, sql_query)
            # Caching import localy for 1 day
            .cached("cache/path")
    )
    # Preproces the data
    .process([
        Transformations.unpack_json(...),
        ...
    ])
    # Predict the values
    .predict()

GitHub

View Github