зеркало из https://github.com/py-why/EconML.git
CATE validation - uplift uniform confidence bands (#840)
Add support for multiplier bootstrap uniform confidence band error bars for uplift curves
This commit is contained in:
Amar Venugopal
GitHub
Родитель
ed4fe33b2b
Коммит
27d3101e23
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@ -147,6 +147,20 @@ CATE Interpreters
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econml.cate_interpreter.SingleTreeCateInterpreter
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econml.cate_interpreter.SingleTreePolicyInterpreter
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.. _validation_api:
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CATE Validation
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---------------
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.. autosummary::
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:toctree: _autosummary
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econml.validate.DRTester
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econml.validate.BLPEvaluationResults
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econml.validate.CalibrationEvaluationResults
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econml.validate.UpliftEvaluationResults
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econml.validate.EvaluationResults
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.. _scorers_api:
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CATE Scorers
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@ -5,7 +5,7 @@ import pandas as pd
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import scipy.stats as st
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from sklearn.ensemble import RandomForestClassifier, GradientBoostingRegressor
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from econml.validate.drtester import DRtester
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from econml.validate.drtester import DRTester
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from econml.dml import DML
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@ -70,7 +70,7 @@ class TestDRTester(unittest.TestCase):
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).fit(Y=Ytrain, T=Dtrain, X=Xtrain)
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# test the DR outcome difference
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my_dr_tester = DRtester(
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my_dr_tester = DRTester(
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model_regression=reg_y,
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model_propensity=reg_t,
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cate=cate
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@ -123,7 +123,7 @@ class TestDRTester(unittest.TestCase):
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).fit(Y=Ytrain, T=Dtrain, X=Xtrain)
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# test the DR outcome difference
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my_dr_tester = DRtester(
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my_dr_tester = DRTester(
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model_regression=reg_y,
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model_propensity=reg_t,
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cate=cate
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@ -148,8 +148,8 @@ class TestDRTester(unittest.TestCase):
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self.assertRaises(ValueError, res.plot_toc, k)
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else: # real treatment, k = 1
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self.assertTrue(res.plot_cal(k) is not None)
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self.assertTrue(res.plot_qini(k) is not None)
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self.assertTrue(res.plot_toc(k) is not None)
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self.assertTrue(res.plot_qini(k, 'ucb2') is not None)
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self.assertTrue(res.plot_toc(k, 'ucb1') is not None)
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self.assertLess(res_df.blp_pval.values[0], 0.05) # heterogeneity
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self.assertGreater(res_df.cal_r_squared.values[0], 0) # good R2
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@ -171,7 +171,7 @@ class TestDRTester(unittest.TestCase):
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).fit(Y=Ytrain, T=Dtrain, X=Xtrain)
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# test the DR outcome difference
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my_dr_tester = DRtester(
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my_dr_tester = DRTester(
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model_regression=reg_y,
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model_propensity=reg_t,
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cate=cate
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@ -193,8 +193,8 @@ class TestDRTester(unittest.TestCase):
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for kwargs in [{}, {'Xval': Xval}]:
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with self.assertRaises(Exception) as exc:
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my_dr_tester.evaluate_cal(kwargs)
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self.assertTrue(
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str(exc.exception) == "Must fit nuisance models on training sample data to use calibration test"
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self.assertEqual(
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str(exc.exception), "Must fit nuisance models on training sample data to use calibration test"
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)
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def test_exceptions(self):
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@ -212,7 +212,7 @@ class TestDRTester(unittest.TestCase):
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).fit(Y=Ytrain, T=Dtrain, X=Xtrain)
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# test the DR outcome difference
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my_dr_tester = DRtester(
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my_dr_tester = DRTester(
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model_regression=reg_y,
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model_propensity=reg_t,
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cate=cate
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@ -223,11 +223,11 @@ class TestDRTester(unittest.TestCase):
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with self.assertRaises(Exception) as exc:
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func()
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if func.__name__ == 'evaluate_cal':
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self.assertTrue(
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str(exc.exception) == "Must fit nuisance models on training sample data to use calibration test"
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self.assertEqual(
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str(exc.exception), "Must fit nuisance models on training sample data to use calibration test"
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)
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else:
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self.assertTrue(str(exc.exception) == "Must fit nuisances before evaluating")
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self.assertEqual(str(exc.exception), "Must fit nuisances before evaluating")
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my_dr_tester = my_dr_tester.fit_nuisance(
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Xval, Dval, Yval, Xtrain, Dtrain, Ytrain
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@ -242,11 +242,11 @@ class TestDRTester(unittest.TestCase):
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with self.assertRaises(Exception) as exc:
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func()
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if func.__name__ == 'evaluate_blp':
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self.assertTrue(
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str(exc.exception) == "CATE predictions not yet calculated - must provide Xval"
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self.assertEqual(
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str(exc.exception), "CATE predictions not yet calculated - must provide Xval"
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)
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else:
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self.assertTrue(str(exc.exception) ==
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self.assertEqual(str(exc.exception),
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"CATE predictions not yet calculated - must provide both Xval, Xtrain")
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for func in [
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@ -256,19 +256,19 @@ class TestDRTester(unittest.TestCase):
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]:
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with self.assertRaises(Exception) as exc:
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func(Xval=Xval)
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self.assertTrue(
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str(exc.exception) == "CATE predictions not yet calculated - must provide both Xval, Xtrain")
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self.assertEqual(
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str(exc.exception), "CATE predictions not yet calculated - must provide both Xval, Xtrain")
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cal_res = my_dr_tester.evaluate_cal(Xval, Xtrain)
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self.assertGreater(cal_res.cal_r_squared[0], 0) # good R2
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with self.assertRaises(Exception) as exc:
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my_dr_tester.evaluate_uplift(metric='blah')
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self.assertTrue(
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str(exc.exception) == "Unsupported metric - must be one of ['toc', 'qini']"
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self.assertEqual(
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str(exc.exception), "Unsupported metric 'blah' - must be one of ['toc', 'qini']"
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)
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my_dr_tester = DRtester(
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my_dr_tester = DRTester(
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model_regression=reg_y,
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model_propensity=reg_t,
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cate=cate
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@ -278,5 +278,11 @@ class TestDRTester(unittest.TestCase):
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qini_res = my_dr_tester.evaluate_uplift(Xval, Xtrain)
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self.assertLess(qini_res.pvals[0], 0.05)
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with self.assertRaises(Exception) as exc:
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qini_res.plot_uplift(tmt=1, err_type='blah')
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self.assertEqual(
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str(exc.exception), "Invalid error type 'blah'; must be one of [None, 'ucb2', 'ucb1']"
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)
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autoc_res = my_dr_tester.evaluate_uplift(Xval, Xtrain, metric='toc')
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self.assertLess(autoc_res.pvals[0], 0.05)
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@ -5,7 +5,9 @@
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A suite of validation methods for CATE models.
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"""
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from .drtester import DRtester
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from .drtester import DRTester
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from .results import BLPEvaluationResults, CalibrationEvaluationResults, UpliftEvaluationResults, EvaluationResults
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__all__ = ['DRtester']
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__all__ = ['DRTester',
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'BLPEvaluationResults', 'CalibrationEvaluationResults', 'UpliftEvaluationResults', 'EvaluationResults']
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@ -8,12 +8,13 @@ from sklearn.model_selection import cross_val_predict, StratifiedKFold, KFold
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from statsmodels.api import OLS
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from statsmodels.tools import add_constant
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from econml.utilities import deprecated
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from .results import CalibrationEvaluationResults, BLPEvaluationResults, UpliftEvaluationResults, EvaluationResults
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from .utils import calculate_dr_outcomes, calc_uplift
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class DRtester:
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class DRTester:
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"""
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Validation tests for CATE models. Includes the best linear predictor (BLP) test as in Chernozhukov et al. (2022),
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the calibration test in Dwivedi et al. (2020), and the QINI coefficient as in Radcliffe (2007).
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@ -46,47 +47,62 @@ class DRtester:
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The calibration r-squared metric is similar to the standard R-square score in that it can take any value
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less than or equal to 1, with scores closer to 1 indicating a better calibrated CATE model.
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**QINI**
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**Uplift Modeling**
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Units are ordered by predicted CATE values and a running measure of the average treatment effect in each cohort is
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kept as we progress through ranks. The QINI coefficient is then the area under the resulting curve, with a value
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of 0 interpreted as corresponding to a model with randomly assigned CATE coefficients. All calculations are
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performed on validation dataset results, using the training set as input.
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kept as we progress through ranks. The resulting TOC curve can then be plotted and its integral calculated and used
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as a measure of true heterogeneity captured by the CATE model; this integral is referred to as the AUTOC (area
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under TOC). The QINI curve is a variant of this curve that also incorporates treatment probability; its integral is
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referred to as the QINI coefficient.
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More formally, the QINI curve is given by the following function:
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More formally, the TOC and QINI curves are given by the following functions:
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.. math::
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\\tau_{TOC}(q) = \\mathrm{Cov}(
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Y^{DR}(g,p),
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\\frac{
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\\mathbb{1}\\{\\hat{\\tau}(Z) \\geq \\hat{\\mu}(q)\\}
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}{
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\\mathrm{Pr}(\\hat{\\tau}(Z) \\geq \\hat{\\mu}(q))
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}
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)
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\\tau_{QINI}(q) = \\mathrm{Cov}(Y^{DR}(g,p), \\mathbb{1}\\{\\hat{\\tau}(Z) \\geq \\hat{\\mu}(q)\\})
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Where :math:`q` is the desired quantile, :math:`\\hat{\\mu}` is the quantile function, and :math:`\\hat{\\tau}` is
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the predicted CATE function.
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:math:`Y^{DR}(g,p)` refers to the doubly robust outcome difference (relative to control) for the given observation.
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The QINI coefficient is then given by:
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The AUTOC and QINI coefficient are then given by:
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.. math::
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AUTOC = \\int_0^1 \\tau_{TOC}(q) dq
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QINI = \\int_0^1 \\tau_{QINI}(q) dq
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Parameters
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----------
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model_regression: estimator
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Nuisance model estimator used to fit the outcome to features. Must be able to implement `fit' and `predict'
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Nuisance model estimator used to fit the outcome to features. Must be able to implement `fit` and `predict`
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methods
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model_propensity: estimator
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Nuisance model estimator used to fit the treatment assignment to features. Must be able to implement `fit'
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method and either `predict' (in the case of binary treatment) or `predict_proba' methods (in the case of
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Nuisance model estimator used to fit the treatment assignment to features. Must be able to implement `fit`
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method and either `predict` (in the case of binary treatment) or `predict_proba` methods (in the case of
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multiple categorical treatments).
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n_splits: integer, default 5
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Number of splits used to generate cross-validated predictions
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cate: estimator
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Fitted conditional average treatment effect (CATE) estimator to be validated.
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cv: int or list, default 5
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Splitter used for cross-validation. Can be either an integer (corresponding to the number of desired folds)
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or a list of indices corresponding to membership in each fold.
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References
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----------
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[Chernozhukov2022] V. Chernozhukov et al.
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Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments
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arXiv preprint arXiv:1712.04802, 2022.
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@ -97,7 +113,6 @@ class DRtester:
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arXiv preprint arXiv:2008.10109, 2020.
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`<https://arxiv.org/abs/2008.10109>`_
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[Radcliffe2007] N. Radcliffe
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Using control groups to target on predicted lift: Building and assessing uplift model.
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Direct Marketing Analytics Journal (2007), pages 14–21.
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@ -175,7 +190,7 @@ class DRtester:
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Generates nuisance predictions and calculates doubly robust (DR) outcomes either by (1) cross-fitting in the
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validation sample, or (2) fitting in the training sample and applying to the validation sample. If Xtrain,
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Dtrain, and ytrain are all not None, then option (2) will be implemented, otherwise, option (1) will be
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implemented. In order to use the `evaluate_cal' method then Xtrain, Dtrain, and ytrain must all be specified.
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implemented. In order to use the `evaluate_cal` method then Xtrain, Dtrain, and ytrain must all be specified.
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Parameters
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----------
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@ -186,12 +201,12 @@ class DRtester:
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the control status be equal to 0, and all other treatments integers starting at 1.
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yval: vector of length n_val
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Outcomes for the validation sample
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Xtrain: (n_train x k) matrix or vector of length n, default ``None``
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Xtrain: (n_train x k) matrix or vector of length n, optional
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Features used in nuisance models for training sample
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Dtrain: vector of length n_train, default ``None''
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Dtrain: vector of length n_train, optional
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Treatment assignment of training sample. Control status must be minimum value. It is recommended to have
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the control status be equal to 0, and all other treatments integers starting at 1.
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ytrain: vector of length n_train, defaul ``None``
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ytrain: vector of length n_train, optional
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Outcomes for the training sample
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Returns
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@ -332,7 +347,7 @@ class DRtester:
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----------
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Xval: (n_val x n_treatment) matrix
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Validation set features to be used to predict (and potentially fit) DR outcomes in CATE model
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Xtrain (n_train x n_treatment) matrix, defaul ``None``
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Xtrain (n_train x n_treatment) matrix, optional
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Training set features used to fit CATE model
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Returns
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@ -359,11 +374,11 @@ class DRtester:
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Parameters
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----------
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Xval: (n_val x n_treatment) matrix, default ``None``
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Validation sample features for CATE model. If not specified, then `fit_cate' method must already have been
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Xval: (n_val x n_treatment) matrix, optional
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Validation sample features for CATE model. If not specified, then `fit_cate` method must already have been
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implemented
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Xtrain: (n_train x n_treatment) matrix, default ``None``
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Training sample features for CATE model. If not specified, then `fit cate' method must already have been
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Xtrain: (n_train x n_treatment) matrix, optional
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Training sample features for CATE model. If not specified, then `fit cate` method must already have been
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implemented (with Xtrain specified)
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n_groups: integer, default 4
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Number of quantile-based groups used to calculate calibration score.
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@ -433,17 +448,17 @@ class DRtester:
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Xtrain: np.array = None
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) -> BLPEvaluationResults:
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"""
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Implements the best linear predictor (BLP) test as in [Chernozhukov2022]. `fit_nusiance' method must already
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Implements the best linear predictor (BLP) test as in [Chernozhukov2022]. `fit_nusiance` method must already
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be implemented.
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Parameters
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----------
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Xval: (n_val x k) matrix, default ``None''
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Validation sample features for CATE model. If not specified, then `fit_cate' method must already have been
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Xval: (n_val x k) matrix, optional
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Validation sample features for CATE model. If not specified, then `fit_cate` method must already have been
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implemented
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Xtrain: (n_train x k) matrix, default ``None''
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Xtrain: (n_train x k) matrix, optional
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Training sample features for CATE model. If specified, then CATE is fitted on training sample and applied
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to Xval. If specified, then Xtrain, Dtrain, Ytrain must have been specified in `fit_nuisance' method (and
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to Xval. If specified, then Xtrain, Dtrain, Ytrain must have been specified in `fit_nuisance` method (and
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vice-versa)
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Returns
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@ -489,29 +504,32 @@ class DRtester:
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Xval: np.array = None,
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Xtrain: np.array = None,
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percentiles: np.array = np.linspace(5, 95, 50),
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metric: str = 'qini'
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metric: str = 'qini',
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n_bootstrap: int = 1000
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) -> UpliftEvaluationResults:
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"""
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Calculates QINI coefficient for the given model as in Radcliffe (2007), where units are ordered by predicted
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Calculates uplift curves and coefficients for the given model, where units are ordered by predicted
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CATE values and a running measure of the average treatment effect in each cohort is kept as we progress
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through ranks. The QINI coefficient is then the area under the resulting curve, with a value of 0 interpreted
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through ranks. The uplift coefficient is then the area under the resulting curve, with a value of 0 interpreted
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as corresponding to a model with randomly assigned CATE coefficients. All calculations are performed on
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validation dataset results, using the training set as input.
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Parameters
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----------
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Xval: (n_val x k) matrix, default ``None''
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Validation sample features for CATE model. If not specified, then `fit_cate' method must already have been
|
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Xval: (n_val x k) matrix, optional
|
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Validation sample features for CATE model. If not specified, then `fit_cate` method must already have been
|
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implemented
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Xtrain: (n_train x k) matrix, default ``None''
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Xtrain: (n_train x k) matrix, optional
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Training sample features for CATE model. If specified, then CATE is fitted on training sample and applied
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to Xval. If specified, then Xtrain, Dtrain, Ytrain must have been specified in `fit_nuisance' method (and
|
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to Xval. If specified, then Xtrain, Dtrain, Ytrain must have been specified in `fit_nuisance` method (and
|
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vice-versa)
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percentiles: one-dimensional array, default ``np.linspace(5, 95, 50)''
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percentiles: one-dimensional array, default ``np.linspace(5, 95, 50)``
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Array of percentiles over which the QINI curve should be constructed. Defaults to 5%-95% in intervals of
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5%.
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metric: string, default 'qini'
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Which type of uplift curve to evaluate. Must be one of ['toc', 'qini']
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n_bootstrap: integer, default 1000
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Number of bootstrap samples to run when calculating uniform confidence bands.
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Returns
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-------
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|
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@ -532,7 +550,8 @@ class DRtester:
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self.cate_preds_val_,
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self.dr_val_,
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percentiles,
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metric
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metric,
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n_bootstrap
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)
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coeffs = [coeff]
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errs = [err]
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|
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@ -546,7 +565,8 @@ class DRtester:
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self.cate_preds_val_[:, k],
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self.dr_val_[:, k],
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percentiles,
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metric
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metric,
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n_bootstrap
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)
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coeffs.append(coeff)
|
||||
errs.append(err)
|
||||
|
|
@ -568,20 +588,25 @@ class DRtester:
|
|||
self,
|
||||
Xval: np.array = None,
|
||||
Xtrain: np.array = None,
|
||||
n_groups: int = 4
|
||||
n_groups: int = 4,
|
||||
n_bootstrap: int = 1000
|
||||
) -> EvaluationResults:
|
||||
"""
|
||||
Implements the best linear prediction (`evaluate_blp'), calibration (`evaluate_cal'), uplift curve
|
||||
('evaluate_uplift') methods
|
||||
Implements the best linear prediction (`evaluate_blp`), calibration (`evaluate_cal`), uplift curve
|
||||
(`evaluate_uplift`) methods
|
||||
|
||||
Parameters
|
||||
----------
|
||||
Xval: (n_cal x k) matrix, default ``None''
|
||||
Validation sample features for CATE model. If not specified, then `fit_cate' method must already have been
|
||||
Xval: (n_cal x k) matrix, optional
|
||||
Validation sample features for CATE model. If not specified, then `fit_cate` method must already have been
|
||||
implemented
|
||||
Xtrain: (n_train x k) matrix, default ``None''
|
||||
Training sample features for CATE model. If not specified, then `fit_cate' method must already have been
|
||||
Xtrain: (n_train x k) matrix, optional
|
||||
Training sample features for CATE model. If not specified, then `fit_cate` method must already have been
|
||||
implemented
|
||||
n_groups: integer, default 4
|
||||
Number of quantile-based groups used to calculate calibration score.
|
||||
n_bootstrap: integer, default 1000
|
||||
Number of bootstrap samples to run when calculating uniform confidence bands for uplift curves.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -595,8 +620,8 @@ class DRtester:
|
|||
|
||||
blp_res = self.evaluate_blp()
|
||||
cal_res = self.evaluate_cal(n_groups=n_groups)
|
||||
qini_res = self.evaluate_uplift(metric='qini')
|
||||
toc_res = self.evaluate_uplift(metric='toc')
|
||||
qini_res = self.evaluate_uplift(metric='qini', n_bootstrap=n_bootstrap)
|
||||
toc_res = self.evaluate_uplift(metric='toc', n_bootstrap=n_bootstrap)
|
||||
|
||||
self.res = EvaluationResults(
|
||||
blp_res=blp_res,
|
||||
|
|
@ -606,3 +631,9 @@ class DRtester:
|
|||
)
|
||||
|
||||
return self.res
|
||||
|
||||
|
||||
@deprecated("DRtester has been renamed 'DRTester' and the old name has been deprecated and will be removed "
|
||||
"in a future release. Please use 'DRTester' instead.")
|
||||
class DRtester(DRTester):
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -20,6 +20,7 @@ class CalibrationEvaluationResults:
|
|||
treatments: list or numpy array of floats
|
||||
Sequence of treatment labels
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cal_r_squared: np.array,
|
||||
|
|
@ -99,6 +100,7 @@ class BLPEvaluationResults:
|
|||
treatments: list or numpy array of floats
|
||||
Sequence of treatment labels
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: List[float],
|
||||
|
|
@ -154,6 +156,7 @@ class UpliftEvaluationResults:
|
|||
Dictionary mapping treatment levels to dataframes containing
|
||||
necessary data for plotting uplift curves
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: List[float],
|
||||
|
|
@ -188,7 +191,7 @@ class UpliftEvaluationResults:
|
|||
}).round(3)
|
||||
return res
|
||||
|
||||
def plot_uplift(self, tmt: Any):
|
||||
def plot_uplift(self, tmt: Any, err_type: str = None):
|
||||
"""
|
||||
Plots uplift curves.
|
||||
|
||||
|
|
@ -197,6 +200,10 @@ class UpliftEvaluationResults:
|
|||
tmt: any (sortable)
|
||||
Name of treatment to plot.
|
||||
|
||||
err_type: str
|
||||
Type of error to plot. Accepted values are normal (None), two-sided uniform confidence band ('ucb2'),
|
||||
or 1-sided uniform confidence band ('ucb1').
|
||||
|
||||
Returns
|
||||
-------
|
||||
matplotlib plot with percentage treated on x-axis and uplift metric (and 95% CI) on y-axis
|
||||
|
|
@ -205,10 +212,30 @@ class UpliftEvaluationResults:
|
|||
raise ValueError(f'Invalid treatment; must be one of {self.treatments[1:]}')
|
||||
|
||||
df = self.curves[tmt].copy()
|
||||
|
||||
if err_type is None:
|
||||
df['95_err'] = 1.96 * df['err']
|
||||
elif err_type == 'ucb2':
|
||||
df['95_err'] = df['uniform_critical_value'] * df['err']
|
||||
elif err_type == 'ucb1':
|
||||
df['95_err'] = df['uniform_one_side_critical_value'] * df['err']
|
||||
else:
|
||||
raise ValueError(f"Invalid error type {err_type!r}; must be one of [None, 'ucb2', 'ucb1']")
|
||||
|
||||
res = self.summary()
|
||||
coeff = round(res.loc[res['treatment'] == tmt]['est'].values[0], 3)
|
||||
err = round(res.loc[res['treatment'] == tmt]['se'].values[0], 3)
|
||||
|
||||
if err_type == 'ucb1':
|
||||
fig = df.plot(
|
||||
kind='scatter',
|
||||
x='Percentage treated',
|
||||
y='value',
|
||||
yerr=[[df['95_err'], np.zeros(len(df))]],
|
||||
ylabel='Gain over Random',
|
||||
title=f"Treatment = {tmt}, Integral = {coeff} +/- {err}"
|
||||
)
|
||||
else:
|
||||
fig = df.plot(
|
||||
kind='scatter',
|
||||
x='Percentage treated',
|
||||
|
|
@ -239,6 +266,7 @@ class EvaluationResults:
|
|||
toc_res: UpliftEvaluationResults object
|
||||
Results object for TOC test
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
cal_res: CalibrationEvaluationResults,
|
||||
|
|
@ -290,7 +318,7 @@ class EvaluationResults:
|
|||
"""
|
||||
return self.cal.plot_cal(tmt)
|
||||
|
||||
def plot_qini(self, tmt: int):
|
||||
def plot_qini(self, tmt: int, err_type: str = None):
|
||||
"""
|
||||
Plots QINI curves.
|
||||
|
||||
|
|
@ -299,13 +327,17 @@ class EvaluationResults:
|
|||
tmt: integer
|
||||
Treatment level to plot
|
||||
|
||||
err_type: str
|
||||
Type of error to plot. Accepted values are normal (None), two-sided uniform confidence band ('ucb2'),
|
||||
or 1-sided uniform confidence band ('ucb1').
|
||||
|
||||
Returns
|
||||
-------
|
||||
matplotlib plot with percentage treated on x-axis and QINI value (and 95% CI) on y-axis
|
||||
"""
|
||||
return self.qini.plot_uplift(tmt)
|
||||
return self.qini.plot_uplift(tmt, err_type)
|
||||
|
||||
def plot_toc(self, tmt: int):
|
||||
def plot_toc(self, tmt: int, err_type: str = None):
|
||||
"""
|
||||
Plots TOC curves.
|
||||
|
||||
|
|
@ -314,8 +346,12 @@ class EvaluationResults:
|
|||
tmt: integer
|
||||
Treatment level to plot
|
||||
|
||||
err_type: str
|
||||
Type of error to plot. Accepted values are normal (None), two-sided uniform confidence band ('ucb2'),
|
||||
or 1-sided uniform confidence band ('ucb1').
|
||||
|
||||
Returns
|
||||
-------
|
||||
matplotlib plot with percentage treated on x-axis and TOC value (and 95% CI) on y-axis
|
||||
"""
|
||||
return self.toc.plot_uplift(tmt)
|
||||
return self.toc.plot_uplift(tmt, err_type)
|
||||
|
|
|
|||
|
|
@ -53,11 +53,14 @@ def calc_uplift(
|
|||
cate_preds_val: np.array,
|
||||
dr_val: np.array,
|
||||
percentiles: np.array,
|
||||
metric: str
|
||||
metric: str,
|
||||
n_bootstrap: int = 1000
|
||||
) -> Tuple[float, float, pd.DataFrame]:
|
||||
"""
|
||||
Helper function for QINI curve generation and QINI coefficient calculation.
|
||||
See documentation for "evaluate_qini" method for more details.
|
||||
Helper function for uplift curve generation and coefficient calculation.
|
||||
Calculates uplift curve points, integral, and errors on both points and integral.
|
||||
Also calculates appropriate critical value multipliers for confidence intervals (via multiplier bootstrap).
|
||||
See documentation for "drtester.evaluate_uplift" method for more details.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
|
|
@ -72,6 +75,8 @@ def calc_uplift(
|
|||
Array of percentiles over which the QINI curve should be constructed. Defaults to 5%-95% in intervals of 5%.
|
||||
metric: string
|
||||
String indicating whether to calculate TOC or QINI; should be one of ['toc', 'qini']
|
||||
n_bootstrap: integer, default 1000
|
||||
Number of bootstrap samples to run when calculating uniform confidence bands.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
|
@ -94,10 +99,19 @@ def calc_uplift(
|
|||
toc[it] = np.mean(dr_val[inds]) - ate # tau(q) := E[Y(1) - Y(0) | tau(X) >= q[it]] - E[Y(1) - Y(0)]
|
||||
toc_psi[it, :] = np.squeeze((dr_val - ate) * (inds / group_prob - 1) - toc[it])
|
||||
else:
|
||||
raise ValueError("Unsupported metric - must be one of ['toc', 'qini']")
|
||||
raise ValueError(f"Unsupported metric {metric!r} - must be one of ['toc', 'qini']")
|
||||
|
||||
toc_std[it] = np.sqrt(np.mean(toc_psi[it] ** 2) / n) # standard error of tau(q)
|
||||
|
||||
w = np.random.normal(0, 1, size=(n, n_bootstrap))
|
||||
mboot = (toc_psi / toc_std.reshape(-1, 1)) @ w / n
|
||||
|
||||
max_mboot = np.max(np.abs(mboot), axis=0)
|
||||
uniform_critical_value = np.percentile(max_mboot, 95)
|
||||
|
||||
min_mboot = np.min(mboot, axis=0)
|
||||
uniform_one_side_critical_value = np.abs(np.percentile(min_mboot, 5))
|
||||
|
||||
coeff_psi = np.sum(toc_psi[:-1] * np.diff(percentiles).reshape(-1, 1) / 100, 0)
|
||||
coeff = np.sum(toc[:-1] * np.diff(percentiles) / 100)
|
||||
coeff_stderr = np.sqrt(np.mean(coeff_psi ** 2) / n)
|
||||
|
|
@ -105,7 +119,9 @@ def calc_uplift(
|
|||
curve_df = pd.DataFrame({
|
||||
'Percentage treated': 100 - percentiles,
|
||||
'value': toc,
|
||||
'err': toc_std
|
||||
'err': toc_std,
|
||||
'uniform_critical_value': uniform_critical_value,
|
||||
'uniform_one_side_critical_value': uniform_one_side_critical_value
|
||||
})
|
||||
|
||||
return coeff, coeff_stderr, curve_df
|
||||
|
|
|
|||
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