Usage

The following sections document the built-in example workflow provided. It is recommended that users follow this workflow to verify proper installation.

Generate Example Data

The simulate sub-command generates suitably formatted input data for testing functionality. It also writes an example config file in YAML format. Both of these output files can serve as templates for building real-world models.

dnattend simulate --config config.yaml DNAttend-example.csv

Train Model

DNAttend trains two models independently; a baseline logistic regression model and a CatBoost model. The baseline model is simple model that acts as reference to assess performance improvements of CatBoost.

dnattend train config.yaml

Evaluate Model

Following initial training, the dnattend test command can be used to assess performance of both the logistic regression and CatBoost models against the hold-out testing data set.

dnattend test config.yaml

Refit Model with All Data

The previous steps have trained two models: a baseline logistic regression model and a more advanced CatBoost. Following parameterisation and assessment of model performance, a final model can be retrained using the entire data set. The user may build a logistic regression or CatBoost model depending on the performance metrics. This choice must be specified by the user in the finalModel: option of the configuration file.

dnattend retrain config.yaml

Generate Predictions

The trained model is now ready to be used. Predictions should be made with the predict module - this ensures the tuned decision threshold is correctly applied when assigning classes. The output of predict includes the decision class (i.e.Attend and DNA) and the underlying probabilities of theses classes.

dnattend predict --verify DNAttend-example.csv catboost-final.pkl > FinalPredictions.csv

Note: the --verify flag is only required when running the example workflow

The output results of this example can be found in the example-data-predictions.csv in the docs/assets folder.

Example Workflow Verification

Following initial installation, it is recommended that users run the example workflow, as described, to verify that the pipeline is functioning as expected. The --verify flag of dnattend predict, as shown above, will check the results against the expected output and notify the user if the output matches or not.

Configuration

DNAttend utilises a single configuration file, in YAML, which documents all model parameter and ensure reproducibility of the analysis. The dnattend simulate command writes an example documented configuration file that the user can use as a template. A copy of this file is shown below and available to download as config.yaml in the assets folder.

input: DNAttend-example.csv    # Path to input data (Mandatory).
target: status                 # Column name of target (Mandatory).
DNAclass: 1                    # Value of target corresponding to DNA.
out: .                         # Output directory to save results.
finalModel: catboost           # Method to train final model (catboost or logistic).
catCols:                       # Column names of categorical features.
    - day
    - priority
    - speciality
    - consultationMedia
    - site
boolCols:                      # Column names of boolean features.
    - firstAppointment
numericCols:                   # Column names of numeric features.
    - age
train_size: 0.7                # Proportion of data for training.
test_size: 0.15                # Proportion of data for testing.
val_size: 0.15                 # Proportion of data for validation.
tuneThresholdBy: f1            # Metric to tune decision threshold (f1 or roc).
cvFolds: 5                     # Hyper-tuning cross-validations.
catboostIterations: 100        # Hyper-tuning CatBoost iterations.
hypertuneIterations: 5         # Hyper-tuning parameter samples.
evalIterations: 10_000         # Upper-limit over-fit iterations.
earlyStoppingRounds: 10        # Over-fit detection early stopping rounds.
seed: 42                       # Seed to ensure workflow reproducibility.