def test_make_classification(self):
X, y = make_classification(random_state=42)
pipe = PipelineCache([('pca', PCA(2)),
('lr', LogisticRegression())],
'cache__')
pipe.fit(X, y)
cache = MLCache.get_cache('cache__')
self.assertEqual(len(cache), 1)
key = list(cache.keys())[0]
self.assertIn("[('X',", key)
self.assertIn("('copy', 'True')", key)
MLCache.remove_cache('cache__')
def test_grid_search_model(self):
X, y = make_classification(random_state=42)
param_grid = [{
'pca': [PCA(2)],
'lr__fit_intercept': [False, True]
}, {
'pca': [SVD(2)],
'lr__fit_intercept': [False, True]
}]
pipe = Pipeline([('pca', 'passthrough'), ('lr', LogisticRegression())])
grid0 = GridSearchCV(pipe, param_grid, error_score='raise')
grid0.fit(X, y)
pipe = PipelineCache([('pca', 'passthrough'),
('lr', LogisticRegression())], 'cache__3')
grid = GridSearchCV(pipe, param_grid, error_score='raise')
grid.fit(X, y)
cache = MLCache.get_cache('cache__3')
# 0.22 increases the number of cached results
self.assertIn(len(cache), (7, 11))
key = list(cache.keys())[0]
self.assertIn("[('X',", key)
self.assertIn("('copy', 'True')", key)
MLCache.remove_cache('cache__3')
self.assertEqual(grid0.best_params_, grid.best_params_)
def test_make_classification(self):
X, y = make_classification(random_state=42)
pipe0 = Pipeline([('pca', PCA(2)), ('lr', LogisticRegression())])
pipe = PipelineCache([('pca', PCA(2)), ('lr', LogisticRegression())],
'cache__')
if hasattr(pipe0, '_check_fit_params'):
pars0 = pipe0._check_fit_params() # pylint: disable=W0212,E1101
pars1 = pipe._check_fit_params() # pylint: disable=W0212,E1101
self.assertEqual(pars0, pars1)
pipe0.fit(X, y)
pipe.fit(X, y)
cache = MLCache.get_cache('cache__')
self.assertEqual(len(cache), 1)
key = list(cache.keys())[0]
self.assertIn("[('X',", key)
self.assertIn("('copy', 'True')", key)
MLCache.remove_cache('cache__')
def test_grid_search_1(self):
X, y = make_classification(random_state=42)
param_grid = {
'pca__n_components': [2, 3],
'pca__whiten': [True, False],
'lr__fit_intercept': [True, False]
}
pipe = Pipeline([('pca', PCA(2)), ('lr', LogisticRegression())])
grid0 = GridSearchCV(pipe, param_grid, error_score='raise', n_jobs=1)
grid0.fit(X, y)
pipe = PipelineCache([('pca', PCA(2)), ('lr', LogisticRegression())],
'cache__1')
grid = GridSearchCV(pipe, param_grid, error_score='raise', n_jobs=1)
grid.fit(X, y)
cache = MLCache.get_cache('cache__1')
# 0.22 increases the number of cached results
self.assertIn(len(cache), (13, 21))
key = list(cache.keys())[0]
self.assertIn("[('X',", key)
self.assertIn("('copy', 'True')", key)
MLCache.remove_cache('cache__1')
self.assertEqual(grid0.best_params_, grid.best_params_)