def test_BaseXClassifier(generate_classification_data):
np.random.seed(RANDOM_SEED)
df, x_names = generate_classification_data()
df['treatment_group_key'] = np.where(df['treatment_group_key'] == CONTROL_NAME, 0, 1)
propensity_model = LogisticRegression()
propensity_model.fit(X=df[x_names].values, y=df['treatment_group_key'].values)
df['propensity_score'] = propensity_model.predict_proba(df[x_names].values)[:, 1]
df_train, df_test = train_test_split(df,
test_size=0.2,
random_state=RANDOM_SEED)
# specify all 4 learners
uplift_model = BaseXClassifier(control_outcome_learner=XGBClassifier(),
control_effect_learner=XGBRegressor(),
treatment_outcome_learner=XGBClassifier(),
treatment_effect_learner=XGBRegressor())
uplift_model.fit(X=df_train[x_names].values,
treatment=df_train['treatment_group_key'].values,
y=df_train[CONVERSION].values)
tau_pred = uplift_model.predict(X=df_test[x_names].values,
p=df_test['propensity_score'].values)
# specify 2 learners
uplift_model = BaseXClassifier(outcome_learner=XGBClassifier(),
effect_learner=XGBRegressor())
uplift_model.fit(X=df_train[x_names].values,
treatment=df_train['treatment_group_key'].values,
y=df_train[CONVERSION].values)
tau_pred = uplift_model.predict(X=df_test[x_names].values,
p=df_test['propensity_score'].values)
# calculate metrics
auuc_metrics = pd.DataFrame({'tau_pred': tau_pred.flatten(),
'W': df_test['treatment_group_key'].values,
CONVERSION: df_test[CONVERSION].values,
'treatment_effect_col': df_test['treatment_effect'].values})
cumgain = get_cumgain(auuc_metrics,
outcome_col=CONVERSION,
treatment_col='W',
treatment_effect_col='treatment_effect_col')
# Check if the cumulative gain when using the model's prediction is
# higher than it would be under random targeting
assert cumgain['tau_pred'].sum() > cumgain['Random'].sum()
def test_BaseXClassifier(generate_classification_data):
np.random.seed(RANDOM_SEED)
df, x_names = generate_classification_data()
df['treatment_group_key'] = np.where(
df['treatment_group_key'] == CONTROL_NAME, 0, 1)
propensity_model = LogisticRegression()
propensity_model.fit(X=df[x_names].values,
y=df['treatment_group_key'].values)
df['propensity_score'] = propensity_model.predict_proba(
df[x_names].values)[:, 1]
df_train, df_test = train_test_split(df,
test_size=0.2,
random_state=RANDOM_SEED)
uplift_model = BaseXClassifier(learner=XGBRegressor(),
control_outcome_learner=XGBClassifier(),
treatment_outcome_learner=XGBClassifier())
uplift_model.fit(X=df_train[x_names].values,
treatment=df_train['treatment_group_key'].values,
y=df_train[CONVERSION].values)
y_pred = uplift_model.predict(X=df_test[x_names].values,
p=df_test['propensity_score'].values)
auuc_metrics = pd.DataFrame(np.c_[y_pred,
df_test['treatment_group_key'].values,
df_test[CONVERSION].values],
columns=['y_pred', 'W', CONVERSION])
cumgain = get_cumgain(auuc_metrics,
outcome_col=CONVERSION,
treatment_col='W',
steps=20)
# Check if the cumulative gain when using the model's prediction is
# higher than it would be under random targeting
assert cumgain['y_pred'].sum() > cumgain['Random'].sum()