def test_FitPredict__predict(self): X = self.X y = self.y lm = LinearRegression() lm_strategy = AdapterForFitPredictAdaptee(lm) lm_strategy.fit(X=X, y=y) result_lm = lm_strategy.predict(X=X) self.assertEqual(list(result_lm.keys()), ['predict']) self.assertEqual(result_lm['predict'].shape, (self.size, 1)) gm = GaussianMixture() gm_strategy = AdapterForFitPredictAdaptee(gm) gm_strategy.fit(X=X) result_gm = gm_strategy.predict(X=X) self.assertEqual(sorted(list(result_gm.keys())), sorted(['predict', 'predict_proba'])) self.assertEqual(result_gm['predict'].shape, (self.size, ))
def test_baseadapter__fit_AdapterForFitPredictAdaptee(self): X = self.X y = self.y lm = LinearRegression() stepstrategy = AdapterForFitPredictAdaptee(lm) self.assertEqual(hasattr(stepstrategy._adaptee, 'coef_'), False) result = stepstrategy.fit(X=X, y=y) self.assertEqual(hasattr(stepstrategy._adaptee, 'coef_'), True) self.assertEqual(stepstrategy, result)