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]