def test_process__setattr__(self):
lm = LinearRegression()
lm_strategy = AdapterForFitPredictAdaptee(lm)
step = Process(lm_strategy)
self.assertEqual(step.copy_X, True)
step.copy_X = False
self.assertEqual(step.copy_X, False)
self.assertEqual('copy_X' in dir(step), False)
def test_process__fit_lm(self):
X = self.X
y = self.y
lm = LinearRegression()
stepstrategy = AdapterForFitPredictAdaptee(lm)
step = Process(stepstrategy)
self.assertEqual(hasattr(step, 'coef_'), False)
result = step.fit(X=X, y=y)
self.assertEqual(hasattr(step, 'coef_'), True)
self.assertEqual(step, result)
def test_process__get_params(self):
lm = LinearRegression()
lm_strategy = AdapterForFitPredictAdaptee(lm)
step = Process(lm_strategy)
result_lm = step.get_params()
self.assertEqual(result_lm, {
'copy_X': True,
'fit_intercept': True,
'n_jobs': 1,
'normalize': False
})
def test_process__fit_dbscan(self):
X = self.X
y = self.y
db = DBSCAN()
stepstrategy = AdapterForAtomicFitPredictAdaptee(db)
step = Process(stepstrategy)
self.assertEqual(hasattr(step, 'core_sample_indices_'), False)
result_fit = step.fit(X=X)
self.assertEqual(hasattr(step, 'core_sample_indices_'), False)
self.assertEqual(step, result_fit)
result_predict = step.predict(X=X)['predict']
self.assertEqual(hasattr(step, 'core_sample_indices_'), True)
self.assertEqual(result_predict.shape, (self.size, ))
def test_process__predict(self):
X = self.X
y = self.y
lm = LinearRegression()
gm = GaussianMixture()
lm_strategy = AdapterForFitPredictAdaptee(lm)
gm_strategy = AdapterForFitPredictAdaptee(gm)
step_lm = Process(lm_strategy)
step_gm = Process(gm_strategy)
step_lm.fit(X=X, y=y)
step_gm.fit(X=X)
result_lm = step_lm.predict(X=X)
result_gm = step_gm.predict(X=X)
self.assertEqual(list(result_lm.keys()), ['predict'])
self.assertEqual(sorted(list(result_gm.keys())),
sorted(['predict', 'predict_proba']))
def test_process__get_fit_signature(self):
lm = LinearRegression()
gm = GaussianMixture()
lm_strategy = AdapterForFitPredictAdaptee(lm)
gm_strategy = AdapterForFitPredictAdaptee(gm)
step_lm = Process(lm_strategy)
step_gm = Process(gm_strategy)
result_lm = step_lm._get_fit_signature()
result_gm = step_gm._get_fit_signature()
self.assertEqual(result_lm, ['X', 'y', 'sample_weight'])
self.assertEqual(result_gm, ['X', 'y'])
def test_process__repr__(self):
lm = LinearRegression()
lm_strategy = AdapterForFitPredictAdaptee(lm)
step = Process(lm_strategy)
result = step.__repr__()
self.assertEqual(result, lm.__repr__())
def test_process__get_predict_signature_lm(self):
lm = LinearRegression()
lm_strategy = AdapterForFitPredictAdaptee(lm)
step_lm = Process(lm_strategy)
result_lm = step_lm._get_predict_signature()
self.assertEqual(result_lm, ['X'])
def test_process__init(self):
lm = LinearRegression()
stepstrategy = AdapterForFitPredictAdaptee(lm)
step = Process(stepstrategy)
self.assertEqual(step._strategy, stepstrategy)