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First steps

You can quickly install atom using pip or conda, see the installation guide. ATOM contains a variety of classes and functions to perform data cleaning, feature engineering, model training, plotting and much more. The easiest way to use everything ATOM has to offer is through one of the main classes:

These two classes are convenient wrappers for the whole machine learning pipeline. Like a sklearn Pipeline, they assemble several steps that can be cross-validated together while setting different parameters. There are, however, some important differences with sklearn's API:

  1. atom is initialized with the data you want to manipulate. This data can be accessed at any moment through atom's data attributes.
  2. The classes in ATOM's API are reached through atom's methods. For example, calling the encode method will initialize an Encoder instance, fit it on the training set and transform the whole dataset.
  3. The transformations are applied immediately after calling the method from atom (there is no fit command). This approach saves lines of code and gives the user a clearer overview and more control over every step in the pipeline.

Let's get started with an example!

First, initialize atom and provide it the data you want to use. You can either input a dataset and let ATOM split the train and test set or provide a train and test set already split. Note that if a dataframe is provided, the indices are reset by atom.

atom = ATOMClassifier(X, y, test_size=0.25)

Apply data cleaning steps through atom's methods. For example, calling impute will handle all missing values in the dataset.

atom.impute(strat_num="median", strat_cat="most_frequent", min_frac_rows=0.1)

When the data is ready for modelling, call the run method. Here, we tune hyperparameters and fit a Random Forest and AdaBoost model to the data.

atom.run(["RF", "AdaB"], metric="accuracy", n_calls=25, n_initial_points=10)

Finally, visualize the result using the integrated plots.

atom.plot_feature_importance(show=10, filename="feature_importance_plot")
atom.plot_prc(title="Precision-recall curve comparison plot")
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