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)