Examples

Working example notebooks are available in the example folder.

Pipeline for Real-World Datasets

from autoum.datasets.utils import get_hillstrom_women_visit
from autoum.pipelines.pipeline_rw import PipelineRW

data = get_hillstrom_women_visit()
pipeline = PipelineRW(
    bayesian_causal_forest=True,
    cv_number_splits=10,
    class_variable_transformation=True,
    generalized_random_forest=True,
    lais_generalization=True,
    max_depth=5,
    min_samples_leaf=50,
    min_samples_treatment=10,
    n_estimators=20,
    plot_figures=True,
    plot_uqc=True,
    rlearner=True,
    run_name="AutoUM",
    run_id=1,
    slearner=True,
    show_title=True,
    traditional=True,
    treatment_dummy=True,
    two_model=True,
    urf_ed=True,
    urf_kl=True,
    urf_chi=True,
    urf_ddp=True,
    urf_cts=True,
    urf_it=True,
    urf_cit=True,
    xlearner=True
)

pipeline.analyze_dataset(data)

Pipeline for Synthetic Datasets

from autoum.pipelines.pipeline_sd import PipelineSD

n_samples = 20000  # 20.000 samples / rows
n_covariates = 20  # 20 covariates / columns
sigma = 0.5  # Covariance of 0.5
treatment_propensity = 0.5  # treatment propensity of 0.5 (i.e. 50:50)
response_rate = 20  # 20% response rate

pipeline = PipelineSD(
    n=n_samples,
    p=n_covariates,
    sigma=sigma,
    threshold=response_rate,
    propensity=treatment_propensity,
    cv_number_splits=5,
    generalized_random_forest=True,
    max_depth=5,
    min_samples_leaf=50,
    min_samples_treatment=10,
    n_estimators=20,
    plot_figures=True,
    plot_uqc=True,
    run_name="Synthetic_Example",
    show_title=True,
    traditional=True,
    two_model=True,
    urf_ed=True
)

data = pipeline.create_synthetic_dataset()
pipeline.analyze_dataset(data)