def test_baseadapter__get_fit_signature(self): lm = LinearRegression() gm = GaussianMixture() lm.__class__ = type('newClass', (type(lm), FitPredictMixin), {}) gm.__class__ = type('newClass', (type(gm), FitPredictMixin), {}) result_lm = lm._get_fit_signature() result_gm = gm._get_fit_signature() self.assertEqual(sorted(result_lm), sorted(['X', 'y', 'sample_weight'])) self.assertEqual(sorted(result_gm), sorted(['X', 'y']))
def test_FitPredict__predict(self): X = self.X y = self.y lm = LinearRegression() lm.__class__ = type('newClass', (type(lm), FitPredictMixin), {}) lm.fit(X=X, y=y) result_lm = lm.predict_dict(X=X) self.assertEqual(list(result_lm.keys()), ['predict']) self.assertEqual(result_lm['predict'].shape, (self.size, 1)) gm = GaussianMixture() gm.__class__ = type('newClass', (type(gm), FitPredictMixin), {}) gm.fit_using_varargs(X=X) result_gm = gm.predict_dict(X=X) self.assertEqual(sorted(list(result_gm.keys())), sorted(['predict', 'predict_proba'])) self.assertEqual(result_gm['predict'].shape, (self.size, ))