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Training

The training phase is where the models are fitted and evaluated. After this, the models are attached to the trainer, and you can use the plotting and predicting methods. The pipeline applies the following steps iteratively for all models:

  1. The optimal hyperparameters for the model are selected using a bayesian optimization algorithm (optional).
  2. The model is fitted on the training set using the best combination of hyperparameters found. After that, the model is evaluated on the tes set.
  3. Calculate various scores on the test set using a bootstrap algorithm (optional).

There are three approaches to run the training.

The direct fashion repeats the aforementioned steps only once, while the other two approaches repeats them more than once. Every approach can be directly called from atom through the run, successive_halving and train_sizing methods respectively.

Models are called through their acronyms, e.g. atom.run(models="RF") will train a Random Forest. If you want to run the same model multiple times, add a tag after the acronym to differentiate them.

atom.run(
    models=["RF1", "RF2"],
    est_params={
        "RF1": {"n_estimators": 100},
        "RF2": {"n_estimators": 200},
    }
)

For example, this pipeline will fit two Random Forest models, one with 100 and the other with 200 decision trees. The models can be accessed through atom.rf1 and atom.rf2. Use tagged models to test how the same model performs when fitted with different parameters or on different data sets. See the Imbalanced datasets example.

Additional things to take into account:

  • If an exception is encountered while fitting an estimator, the pipeline will automatically jump to the next model. The exceptions are stored in the errors attribute. Note that when a model is skipped, there is no model subclass for that estimator.
  • When showing the final results, a ! indicates the highest score and a ~ indicates that the model is possibly overfitting (training set has a score at least 20% higher than the test set).
  • The winning model (the one with the highest mean_bootstrap or metric_test) can be accessed through the winner attribute.


Metric

ATOM uses sklearn's scorers for model evaluation. A scorer consists of a metric function and some parameters that define the scorer's properties , such as if a higher or lower score is better (score or loss function) or if the function needs probability estimates or rounded predictions (see the make_scorer function). ATOM lets you define the scorer for the pipeline in three ways:

  • The metric parameter is the name of a predefined scorer.
  • The metric parameter is a function with signature metric(y, y_pred). In this case, use the greater_is_better, needs_proba and needs_threshold parameters to specify the scorer's properties.
  • The metric parameter is a scorer object.

Note that all scorers follow the convention that higher return values are better than lower return values. Thus, metrics which measure the distance between the model and the data (i.e. loss functions), like max_error or mean_squared_error, will return the negated value of the metric.

Predefined scorers
ATOM accepts all of sklearn's SCORERS as well as some custom acronyms and custom scorers. Since some of sklearn's scorers have quite long names and ATOM is all about lazyfast experimentation, the package provides acronyms for some of the most commonly used ones. These acronyms are case-insensitive and can be used in the metric parameter instead of the scorer's full name, e.g. atom.run("LR", metric="BA") will use balanced_accuracy. The available acronyms are:

  • "AP" for "average_precision"
  • "BA" for "balanced_accuracy"
  • "AUC" for "roc_auc"
  • "LogLoss" for "neg_log_loss"
  • "EV" for "explained_variance"
  • "ME" for "max_error"
  • "MAE" for "neg_mean_absolute_error"
  • "MSE" for "neg_mean_squared_error"
  • "RMSE" for "neg_root_mean_squared_error"
  • "MSLE" for "neg_mean_squared_log_error"
  • "MEDAE" for "neg_median_absolute_error"
  • "MAPE" for "neg_mean_absolute_percentage_error"
  • "POISSON" for "neg_mean_poisson_deviance"
  • "GAMMA" for "neg_mean_gamma_deviance"

ATOM also provides some extra common metrics for binary classification tasks.

  • "TN" for True Negatives
  • "FP" for False Positives
  • "FN" for False Negatives
  • "TP" for True Positives
  • "FPR" for False Positive rate (fall-out)
  • "TPR" for True Positive Rate (sensitivity, recall)
  • "TNR" for True Negative Rate (specificity)
  • "FNR" for False Negative Rate (miss rate)
  • "Lift" for Lift
  • "MCC" for Matthews Correlation Coefficient (also for multiclass classification)

Multi-metric runs
Sometimes it is useful to measure the performance of the models in more than one way. ATOM lets you run the pipeline with multiple metrics at the same time. To do so, provide the metric parameter with a list of desired metrics, e.g. atom.run("LDA", metric=["r2", "mse"]). If you provide metric functions, don't forget to also provide a sequence of values to the greater_is_better, needs_proba and needs_threshold parameters, where the n-th value in corresponds to the n-th function. If you leave them as a single value, that value will apply to every provided metric.

When fitting multi-metric runs, the resulting scores will return a list of metrics. For example, if you provided three metrics to the pipeline, atom.knn.metric_bo could return [0.8734, 0.6672, 0.9001]. Only the first metric of a multi-metric run is used to evaluate every step of the bayesian optimization and to select the winning model.

Info

Some plots let you choose which of the metrics to show using the metric parameter.


Automated feature scaling

Models that require feature scaling will automatically do so before training, if the data is not already scaled. The data is considered scaled if it has one of the following prerequisites:

  • The mean value over the mean of all columns is <0.05 and the mean of the standard deviation over all columns lies between 0.93 and 1.07.
  • There is a transformer in the pipeline whose __name__ contains the word scaler.

The scaling is applied using a Scaler with default parameters. It can be accessed from the model through the scaler attribute. The scaled dataset can be examined through the model's data attributes. Use the available_models method to see which models require feature scaling.


Parameter customization

By default, the parameters every estimator uses are the same default parameters they get from their respective packages. To select different ones, use est_params. There are two ways to add custom parameters to the models: adding them directly to the dictionary as key-value pairs or through various dictionaries with the model names as keys.

Adding the parameters directly to est_params will share them across all models in the pipeline. In this example, both the XGBoost and the LightGBM model will use n_estimators=200. Make sure all the models do have the specified parameters or an exception will be raised!

atom.run(models=["XGB", "LGB"], est_params={"n_estimators": 200})

To specify parameters per model, use the model name as key and a dict of the parameters as value. In this example, the XGBoost model will use n_estimators=200 and the Multi-layer Perceptron will use one hidden layer with 75 neurons.

atom.run(
    models=["XGB", "MLP"],
    est_params={
        "XGB": {"n_estimators": 200},
        "MLP": {"hidden_layer_sizes": (75,)},
    }
)

Some estimators allow you to pass extra parameters to the fit method (besides X and y). This can be done adding _fit at the end of the parameter. For example, to change XGBoost's verbosity, we can run:

atom.run(models="XGB", est_params={"verbose_fit": True})

Note

If a parameter is specified through est_params, it is ignored by the bayesian optimization!


Hyperparameter tuning

In order to achieve maximum performance, it's important to tune an estimator's hyperparameters before training it. ATOM provides hyperparameter tuning using a bayesian optimization (BO) approach implemented with scikit-optimize. The BO is optimized on the first metric provided with the metric parameter. Each step is either computed by cross-validation on the complete training set or by randomly splitting the training set every iteration into a (sub) training and validation set. This process can create some minimum data leakage towards specific parameters (since the estimator is evaluated on data that is used to train the next estimator), but it ensures maximal use of the provided data. However, the leakage is not present in the independent test set, thus the final score of every model is unbiased. Note that, if the dataset is relatively small, the BO's best score can consistently be lower than the final score on the test set due to the considerable fewer instances on which it is trained.

There are many possibilities to tune the BO to your liking. Use n_calls and n_initial_points to determine the number of iterations that are performed randomly at the start (exploration) and the number of iterations spent optimizing (exploitation). If n_calls is equal to n_initial_points, every iteration of the BO will select its hyperparameters randomly. This means the algorithm is technically performing a random search.

Extra things to take into account:

  • The n_calls parameter includes the iterations in n_initial_points, i.e. calling atom.run(models="LR", n_calls=20, n_intial_points=10) will run 20 iterations of which the first 10 are random.
  • If n_initial_points=1, the first call is equal to the estimator's default parameters.
  • The train/validation splits are different per call but equal for all models.
  • Re-evaluating the objective function at the same point automatically skips the calculation and returns the same score as the equivalent call.

Tip

The hyperparameter tuning output can become quite wide for models with many hyperparameters. If you are working in a Jupyter Notebook, you can change the output's width running the following code in a cell: 

from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

Other settings can be changed through the bo_params parameter, a dictionary where every key-value combination can be used to further customize the BO.

By default, the hyperparameters and corresponding dimensions per model are predefined by ATOM. Use the dimensions key to use custom ones. Just like with est_params, you can share the same dimensions across models or use a dictionary with the model names as keys to specify the dimensions for every individual model. Note that the provided search space dimensions must be compliant with skopt's API.

atom.run(
    models="LR",
    n_calls=30,
    bo_params={"dimensions": [Integer(100, 1000, name="max_iter")]},
)

Warning

Keras' models can only use 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. Read here more about deep learning models.

The majority of skopt's callbacks to stop the optimizer early can be accessed through bo_params. Other callbacks can be included through the callbacks key.

atom.run(
    models="LR",
    n_calls=30,
    bo_params={"callbacks": custom_callback()},
)

It's also possible to include additional parameters for skopt's optimizer as key-value pairs.

atom.run("LR", n_calls=10, bo_params={"acq_func": "EI"})


Bootstrapping

After fitting the estimator, you can assess the robustness of the model using the bootstrap technique. This technique creates several new data sets selecting random samples from the training set (with replacement) and evaluates them on the test set. This way we get a distribution of the performance of the model. The sets are the same for every model. The number of sets can be chosen through the n_bootstrap parameter.

Tip

Use the plot_results method to plot the boostrap scores in a boxplot.


Early stopping

XGBoost, LightGBM and CatBoost allow in-training evaluation. This means that the estimator is evaluated after every round of the training, and that the training is stopped early if it didn't improve in the last early_stopping rounds. This can save the pipeline much time that would otherwise be wasted on an estimator that is unlikely to improve further. Note that this technique is applied both during the BO and at the final fit on the complete training set.

There are two ways to apply early stopping on these models:

  • Through the early_stopping key in bo_params. This approach applies early stopping to all models in the pipeline and allows the input of a fraction of the total number of rounds.
  • Filling the early_stopping_rounds parameter directly in est_params. Don't forget to add _fit to the parameter to call it from the fit method.

After fitting, the model gets the evals attribute, a dictionary of the train and test performances per round (also if early stopping wasn't applied). Click here for an example using early stopping.

Tip

Use the plot_evals method to plot the in-training evaluation on the train and test set.


Successive halving

Successive halving is a bandit-based algorithm that fits N models to 1/N of the data. The best half are selected to go to the next iteration where the process is repeated. This continues until only one model remains, which is fitted on the complete dataset. Beware that a model's performance can depend greatly on the amount of data on which it is trained. For this reason, we recommend only to use this technique with similar models, e.g. only using tree-based models.

Use successive halving through the SuccessiveHalvingClassifier/SuccessiveHalvingRegressor classes or from atom via the successive_halving method. Consecutive runs of the same model are saved with the model's acronym followed by the number of models in the run. For example, a Random Forest in a run with 4 models would become model RF4.

Click here for a successive halving example.

Tip

Use the plot_successive_halving method to see every model's performance per iteration of the successive halving.


Train sizing

When training models, there is usually a trade-off between model performance and computation time, that is regulated by the number of samples in the training set. Train sizing can be used to create insights in this trade-off, and help determine the optimal size of the training set. The models are fitted multiple times, ever-increasing the number of samples in the training set.

Use train sizing through the TrainSizingClassifier/TrainSizingRegressor classes or from atom via the train_sizing method. The number of iterations and the number of samples per training can be specified with the train_sizes parameter. Consecutive runs of the same model are saved with the model's acronym followed by the fraction of rows in the training set (the . is removed from the fraction!). For example, a Random Forest in a run with 80% of the training samples would become model RF08.

Click here for a train sizing example.

Tip

Use the plot_learning_curve method to see the model's performance per size of the training set.

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