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, ))
def test_FitPredict__get_predict_signature(self):
lm = LinearRegression()
lm.__class__ = type('newClass', (type(lm), FitPredictMixin), {})
result_lm = lm._get_predict_signature()
self.assertEqual(result_lm, ['X'])
