def grid_tmva(score_function):
grid_param = OrderedDict({"MaxDepth": [4, 5], "NTrees": [10, 20]})
generator = SubgridParameterOptimizer(grid_param)
scorer = FoldingScorer(score_function)
from rep.estimators import TMVAClassifier
grid = GridOptimalSearchCV(TMVAClassifier(features=['column0', 'column1']), generator, scorer)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
assert params[key] == grid.generator.best_params_[key]
def grid_tmva(score_function):
grid_param = OrderedDict({"MaxDepth": [4, 5], "NTrees": [10, 20]})
generator = SubgridParameterOptimizer(grid_param)
scorer = FoldingScorer(score_function)
from rep.estimators import TMVAClassifier
grid = GridOptimalSearchCV(TMVAClassifier(features=['column0', 'column1']),
generator, scorer)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
assert params[key] == grid.generator.best_params_[key]
def grid_custom(custom):
grid_param = OrderedDict({"n_estimators": [10, 20],
"learning_rate": [0.1, 0.05],
'features': [['column0', 'column1'], ['column0', 'column1', 'column2']]})
generator = SubgridParameterOptimizer(grid_param)
grid = GridOptimalSearchCV(SklearnClassifier(clf=AdaBoostClassifier(),
features=['column0', 'column1']), generator, custom)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
if key in params:
assert params[key] == grid.generator.best_params_[key]
else:
assert params['clf__' + key] == grid.generator.best_params_[key]
def grid_sklearn(score_function):
grid_param = OrderedDict({"n_estimators": [10, 20],
"learning_rate": [0.1, 0.05],
'features': [['column0', 'column1'], ['column0', 'column1', 'column2']]})
generator = RegressionParameterOptimizer(grid_param)
scorer = FoldingScorer(score_function)
grid = GridOptimalSearchCV(SklearnClassifier(clf=AdaBoostClassifier()), generator, scorer)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
if key in params:
assert params[key] == grid.generator.best_params_[key]
else:
assert params['clf__' + key] == grid.generator.best_params_[key]
def grid_custom(custom):
grid_param = OrderedDict({
"n_estimators": [10, 20],
"learning_rate": [0.1, 0.05],
'features': [['column0', 'column1'], ['column0', 'column1', 'column2']]
})
generator = SubgridParameterOptimizer(grid_param)
grid = GridOptimalSearchCV(
SklearnClassifier(clf=AdaBoostClassifier(),
features=['column0', 'column1']), generator, custom)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
if key in params:
assert params[key] == grid.generator.best_params_[key]
else:
assert params['clf__' + key] == grid.generator.best_params_[key]
def grid_sklearn(score_function):
grid_param = OrderedDict({
"n_estimators": [10, 20],
"learning_rate": [0.1, 0.05],
'features': [['column0', 'column1'], ['column0', 'column1', 'column2']]
})
generator = RegressionParameterOptimizer(grid_param)
scorer = FoldingScorer(score_function)
grid = GridOptimalSearchCV(SklearnClassifier(clf=AdaBoostClassifier()),
generator, scorer)
cl = check_grid(grid, False, False, False)
assert 1 <= len(cl.features) <= 3
params = cl.get_params()
for key in grid_param:
if key in params:
assert params[key] == grid.generator.best_params_[key]
else:
assert params['clf__' + key] == grid.generator.best_params_[key]
