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Models

Predefined models

ATOM provides many models for classification and regression tasks that can be used to fit the data in the pipeline. After fitting, a class containing the underlying estimator is attached to the trainer as an attribute. We refer to these "subclasses" as models. Apart from the estimator, the models contain a variety of attributes and methods to help you understand how the underlying estimator performed. They can be accessed using their acronyms, e.g. atom.LGB to access the LightGBM's model. The available models and their corresponding acronyms are:

Tip

The acronyms are case-insensitive, e.g. atom.lgb also calls the LightGBM's model.

Warning

The models can not be initialized directly by the user! Use them only through the trainers.


Custom models

It is also possible to create your own models in ATOM's pipeline. For example, imagine we want to use sklearn's RANSACRegressor estimator (note that is not included in ATOM's predefined models). There are two ways to achieve this:

  • Using ATOMModel (recommended). With this approach you can pass the required model characteristics to the pipeline.
from atom import ATOMRegressor, ATOMModel
from sklearn.linear_model import RANSACRegressor

ransac = ATOMModel(
    models=RANSACRegressor,
    acronym="RANSAC",
    fullname="Random Sample Consensus",
    needs_scaling=True,
)

atom = ATOMRegressor(X, y)
atom.run(ransac)
  • Using the estimator's class or an instance of the class. This approach will also call ATOMModel under the hood, but it will leave its parameters to their default values.
from atom import ATOMRegressor
from sklearn.linear_model import RANSACRegressor

atom = ATOMRegressor(X, y)
atom.run(RANSACRegressor)

Additional things to take into account:

  • Custom models can be accessed through their acronym like any other model, e.g. atom.ransac in the example above.
  • Custom models are not restricted to sklearn estimators, but they should follow sklearn's API, i.e. have a fit and predict method.
  • Parameter customization (for the initializer) is only possible for custom models which provide an estimator that has a set_params() method, i.e. it's a child class of BaseEstimator.
  • Hyperparameter optimization for custom models is ignored unless appropriate dimensions are provided through bo_params.
  • If the estimator has a n_jobs and/or random_state parameter that is left to its default value, it will automatically adopt the values from the trainer it's called from.


Deep learning

Deep learning models can be used through ATOM's custom models as long as they follow sklearn's API. For example, models implemented with the Keras package should use the scikeras wrappers KerasClassifier or KerasRegressor.

Many deep learning use cases, for example in computer vision, use datasets with more than 2 dimensions, e.g. image data can have shape (n_samples, length, width, rgb). These data structures are not intended to store in a two-dimensional pandas dataframe, but, since ATOM requires a dataframe for its internal API, datasets with more than two dimensions are stored in a single column called "multidim feature", where every row contains one (multidimensional) sample. Note that the data cleaning, feature engineering and some plotting methods are unavailable when this is the case.

See in this example how to use ATOM to train and validate a Convolutional Neural Network implemented with Keras.

Warning

Keras' models can only use custom hyperparameter tuning when n_jobs=1 or bo_params={"cv": 1}. Using n_jobs > 1 and cv > 1 raises a PicklingError due to incompatibilities of the APIs.


Ensembles

Ensemble models use multiple estimators to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. ATOM implements two ensemble techniques: voting and stacking. Click here to see an example that uses ensemble models.

If the ensemble's underlying estimator is a model that used automated feature scaling, it's added as a Pipeline containing the scaler and estimator. If an mlflow experiment is active, the ensembles start their own run, just like the predefined models do.

Warning

Combining models trained on different branches into one ensemble is not allowed and will raise an exception.

Voting

The idea behind voting is to combine the predictions of conceptually different models to make new predictions. Such a technique can be useful for a set of equally well performing models in order to balance out their individual weaknesses. Read more in sklearn's documentation.

A voting model is created from a trainer through the voting method. The voting model is added automatically to the list of models in the pipeline, under the Vote acronym. The underlying estimator is a custom adaptation of VotingClassifier or VotingRegressor depending on the task. The differences between ATOM's and sklearn's implementation are:

  • ATOM's implementation doesn't fit estimators if they're already fitted.
  • ATOM's instance is considered fitted at initialization when all underlying estimators are.
  • ATOM's VotingClassifier doesn't implement a LabelEncoder to encode the target column.

The two estimators are customized in this way to save time and computational resources, since the classes are always initialized with fitted estimators. As a consequence of this, the VotingClassifier can not use sklearn's build-in LabelEncoder for the target column since it can't be fitted when initializing the class. For the vast majority of use cases, the changes will have no effect. If you want to export the estimator and retrain it on different data, just make sure to clone the underlying estimators first.


Stacking

Stacking is a method for combining estimators to reduce their biases. More precisely, the predictions of each individual estimator are stacked together and used as input to a final estimator to compute the prediction. Read more in sklearn's documentation.

A stacking model is created from a trainer through the stacking method. The stacking model is added automatically to the list of models in the pipeline, under the Stack acronym. The underlying estimator is a custom adaptation of StackingClassifier or StackingRegressor depending on the task. The only difference between ATOM's and sklearn's implementation is that ATOM's implementation doesn't fit estimators if they're already fitted. The two estimators are customized in this way to save time and computational resources, since the classes are always initialized with fitted estimators. For the vast majority of use cases, the changes will have no effect. If you want to export the estimator and retrain it on different data, just make sure to clone the underlying estimators first.

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