def testCausalCumulativePeriods(self):
"""Tests whether model for response has correct coefficients."""
# Fully set up a TBR object.
tbr_model = tbr.TBR()
target = self.key_response
# Engineer some 'causal' costs in the cooldown period.
data = self.data.copy()
cool_index = data[self.key_period] == 2
treat_index = data[self.key_group] == 2
data.loc[(cool_index & treat_index), target] += 100.0
tbr_model.fit(data,
target,
key_response=self.key_response,
key_cost=self.key_cost,
key_group=self.key_group,
key_period=self.key_period,
key_date=self.key_date)
dist_test = tbr_model.causal_cumulative_distribution(periods=(1))
dist_cool = tbr_model.causal_cumulative_distribution(periods=(1, 2))
val_test = dist_test.mean()[-1]
val_cool = dist_cool.mean()[-1]
self.assertLessEqual(val_test, val_cool)
def testPeriodIndexFailsWithEmptyPeriods(self):
"""Tests making a period index for an empty iterable raises a ValueError."""
# Fully set up a TBR object.
tbr_model = tbr.TBR()
target = self.key_response
tbr_model.fit(self.data,
target,
key_response=self.key_response,
key_cost=self.key_cost,
key_group=self.key_group,
key_period=self.key_period,
key_date=self.key_date)
with self.assertRaises(ValueError):
tbr_model._make_period_index([])
def testPeriodIndexWorksForZero(self):
"""Tests making a period index for an empty iterable raises a ValueError."""
# Fully set up a TBR object.
tbr_model = tbr.TBR()
target = self.key_response
tbr_model.fit(self.data,
target,
key_response=self.key_response,
key_cost=self.key_cost,
key_group=self.key_group,
key_period=self.key_period,
key_date=self.key_date)
num_in_period = sum(tbr_model.analysis_data[self.key_period] == 0)
index_count = sum(tbr_model._make_period_index(0))
self.assertEqual(index_count, num_in_period)
def testAnalysisDataGenerated(self):
"""Checks whether the salesandcost example data is available."""
# Fully set up a TBR object.
tbr_model = tbr.TBR()
target = self.key_response
tbr_model.fit(self.data,
target,
key_response=self.key_response,
key_cost=self.key_cost,
key_group=self.key_group,
key_period=self.key_period,
key_date=self.key_date)
constructed_cols = set(tbr_model.analysis_data.keys())
correct_cols = {target, self.key_period}
self.assertCountEqual(constructed_cols, correct_cols)
def testSemanticsAvailable(self):
"""Check if semantics for the data are available."""
# Fully set up a TBR object.
tbr_model = tbr.TBR()
target = self.key_response
tbr_model.fit(self.data,
target,
key_response=self.key_response,
key_cost=self.key_cost,
key_group=self.key_group,
key_period=self.key_period,
key_date=self.key_date)
# Check one member of each of the col names, group and period semantics.
self.assertEqual(tbr_model.df_names.group, self.key_group)
self.assertEqual(tbr_model.groups.treatment, self.groups.treatment)
self.assertEqual(tbr_model.periods.cooldown, self.periods.cooldown)
def testResponseModelCorrect(self):
"""Tests whether model for response has correct coefficients."""
# Fully set up a TBR object.
tbr_model = tbr.TBR()
target = self.key_response
tbr_model.fit(self.data,
target,
key_response=self.key_response,
key_cost=self.key_cost,
key_group=self.key_group,
key_period=self.key_period,
key_date=self.key_date)
# Extract slope coefficient from python model.
response_coef_py = tbr_model.pre_period_model.params[1]
# Slope coefficient under the R package.
response_coef_r = 0.9997001
self.assertAlmostEqual(response_coef_py, response_coef_r)