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Fix incorrect effect_learner_objective in XGBRRegressor #504

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Apr 30, 2022
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Fix incorrect effect_learner_objective in XGBRRegressor #504

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4 changes: 2 additions & 2 deletions causalml/inference/meta/rlearner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -516,7 +516,7 @@ def __init__(
early_stopping=True,
test_size=0.3,
early_stopping_rounds=30,
effect_learner_objective="rank:pairwise",
effect_learner_objective="reg:squarederror",
effect_learner_n_estimators=500,
random_state=42,
*args,
Expand All @@ -531,7 +531,7 @@ def __init__(
early_stopping_rounds (int, optional): validation metric needs to improve at least once in every
early_stopping_rounds round(s) to continue training
effect_learner_objective (str, optional): the learning objective for the effect learner
(default = 'rank:pairwise')
(default = 'reg:squarederror')
effect_learner_n_estimators (int, optional): number of trees to fit for the effect learner (default = 500)
"""

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22 changes: 14 additions & 8 deletions tests/test_meta_learners.py
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Expand Up @@ -840,18 +840,24 @@ def test_BaseRClassifier_with_sample_weights(generate_classification_data):
# higher than it would be under random targeting
assert cumgain["tau_pred"].sum() > cumgain["Random"].sum()


def test_XGBRegressor_with_sample_weights(generate_regression_data):
y, X, treatment, tau, b, e = generate_regression_data()

weights = np.random.rand(y.shape[0])

# Check if XGBRRegressor successfully produces treatment effect estimation
# when sample_weight is passed
uplift_model = XGBRRegressor()
uplift_model.fit(
X=df_train[x_names].values,
p=df_train["propensity_score"].values,
treatment=df_train["treatment_group_key"].values,
y=df_train[CONVERSION].values,
sample_weight=df_train["sample_weights"],
)
tau_pred = uplift_model.predict(X=df_test[x_names].values)
assert len(tau_pred) == len(df_test["sample_weights"].values)
X=X,
p=e,
treatment=treatment,
y=y,
sample_weight=weights,
)
tau_pred = uplift_model.predict(X=X)
assert len(tau_pred) == len(weights)


def test_pandas_input(generate_regression_data):
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