SparkAutoML 0.0.15

For an easy implementation of spark's machine learning library

SparkAutoML

This is a TRIAL version

The main idea of the package is to easily build and deploy pyspark's machine learning library

If you want to contribute please reach out to [e-mail]

Thank you !

https://user-images.githubusercontent.com/19522276/163658399-17786b20-0208-44ff-b111-98b74ab34d25.mp4

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How to install

PySpark's version 3.2.1 or higher is required

pip install SparkAutoML

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How to use

|Binary Classifier|

1: Import the classifier

from SparkAutoML.ml_module.BinaryClassifier_module.classifier_file import BClassifier

2: Setup the experimental

bcf = BClassifier(
    training_data=train, # spark data frame
    hold_out_data=test,  # spark data frame
    target_feature="target",  # target feature
    numeric_features=["num_1","num_3"], # list of all numeric features
    categorical_features=["cat_4", "cat_2"], # list of all categorical features
)

3: Create a model, e.g. random forest classifier

bcf.create_model("rfc") 

OR

Compare multiple models

bcf.compare_models(sort='auc')

4: Make predictions

bcf.predict_model(unseen_data # spark data frame)

5: Access the entire pipeline

bcf.fitted_pipeline