def _make_selection_obj(self, data_table):
statistics_param = StatisticsParam(statistics="summary")
statistics_param.check()
print(statistics_param.statistics)
test_obj = DataStatistics()
test_obj.model_param = statistics_param
test_obj._init_model(statistics_param)
test_obj.fit(data_table)
adapter = adapter_factory(consts.STATISTIC_MODEL)
meta_obj = test_obj.export_model()['StatisticMeta']
param_obj = test_obj.export_model()['StatisticParam']
iso_model = adapter.convert(meta_obj, param_obj)
selection_obj = BaseHeteroFeatureSelection()
selection_obj.isometric_models = {consts.STATISTIC_MODEL: iso_model}
return selection_obj
def test_something(self):
statistics_param = StatisticsParam(statistics="summary")
statistics_param.check()
print(statistics_param.statistics)
test_data = self.gen_data(1000, 16)
test_obj = DataStatistics()
test_obj.model_param = statistics_param
test_obj._init_model(statistics_param)
test_obj.fit(test_data)
static_result = test_obj.summary()
stat_res_1 = static_result[self.header[0]]
self.assertTrue(self._float_equal(stat_res_1['sum'], np.sum(self.col_1)))
self.assertTrue(self._float_equal(stat_res_1['max'], np.max(self.col_1)))
self.assertTrue(self._float_equal(stat_res_1['mean'], np.mean(self.col_1)))
self.assertTrue(self._float_equal(stat_res_1['stddev'], np.std(self.col_1)))
self.assertTrue(self._float_equal(stat_res_1['min'], np.min(self.col_1)))