diff options
| -rw-r--r-- | gensvm/gridsearch.py | 120 | ||||
| -rw-r--r-- | test/test_gridsearch.py | 42 |
2 files changed, 150 insertions, 12 deletions
diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py index 3fdfa9a..ed12b97 100644 --- a/gensvm/gridsearch.py +++ b/gensvm/gridsearch.py @@ -284,10 +284,15 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): Parameters ---------- - param_grid : dict or list of dicts - Dictionary of parameter names (strings) as keys and lists of parameter - settings to evaluate as values, or a list of such dicts. The GenSVM - model will be evaluated at all combinations of the parameters. + param_grid : string, dict, or list of dicts + If a string, it must be either 'tiny', 'small', or 'full' to load the + predefined parameter grids (see the functions :func:`load_grid_tiny`, + :func:`load_grid_small`, and :func:`load_grid_full`). + + Otherwise, a dictionary of parameter names (strings) as keys and lists + of parameter settings to evaluate as values, or a list of such dicts. + The GenSVM model will be evaluated at all combinations of the + parameters. scoring : string, callable, list/tuple, dict or None A single string (see :ref:`scoring_parameter`) or a callable (see @@ -491,7 +496,7 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): def __init__( self, - param_grid, + param_grid="tiny", scoring=None, iid=True, cv=None, @@ -501,6 +506,15 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): ): self.param_grid = param_grid + if isinstance(self.param_grid, str): + if self.param_grid == "tiny": + self.param_grid = load_grid_tiny() + elif self.param_grid == "small": + self.param_grid = load_grid_small() + elif self.param_grid == "full": + self.param_grid = load_grid_full() + else: + raise ValueError("Unknown param grid %r" % self.param_grid) _check_param_grid(self.param_grid) _validate_param_grid(self.param_grid) @@ -644,8 +658,100 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): return self.best_estimator_.predict(X) -def load_default_grid(): - """Load the default parameter grid for GenSVM +def load_grid_tiny(): + """ Load a tiny parameter grid for the GenSVM grid search + + This function returns a parameter grid to use in the GenSVM grid search. + This grid was obtained by analyzing the experiments done for the GenSVM + paper and selecting the configurations that achieve accuracy within the + 95th percentile on over 90% of the datasets. It is a good start for a + parameter search with a reasonably high chance of achieving good + performance on most datasets. + + Note that this grid is only tested to work well in combination with the + linear kernel. + + Returns + ------- + + pg : list + List of 10 parameter configurations that are likely to perform + reasonably well. + + """ + + pg = [ + {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -16)], "weights": ["unit"]}, + {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -18)], "weights": ["unit"]}, + {"p": [2.0], "kappa": [0.5], "lmd": [pow(2, -18)], "weights": ["unit"]}, + { + "p": [2.0], + "kappa": [5.0], + "lmd": [pow(2, -18)], + "weights": ["group"], + }, + { + "p": [2.0], + "kappa": [-0.9], + "lmd": [pow(2, -18)], + "weights": ["unit"], + }, + {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -14)], "weights": ["unit"]}, + { + "p": [2.0], + "kappa": [0.5], + "lmd": [pow(2, -18)], + "weights": ["group"], + }, + { + "p": [1.5], + "kappa": [-0.9], + "lmd": [pow(2, -18)], + "weights": ["unit"], + }, + {"p": [2.0], "kappa": [0.5], "lmd": [pow(2, -16)], "weights": ["unit"]}, + { + "p": [2.0], + "kappa": [0.5], + "lmd": [pow(2, -16)], + "weights": ["group"], + }, + ] + return pg + + +def load_grid_small(): + """Load a small parameter grid for GenSVM + + This function loads a default parameter grid to use for the #' GenSVM + gridsearch. It contains all possible combinations of the following #' + parameter sets:: + + pg = { + 'p': [1.0, 1.5, 2.0], + 'lmd': [1e-8, 1e-6, 1e-4, 1e-2, 1], + 'kappa': [-0.9, 0.5, 5.0], + 'weights': ['unit', 'group'], + } + + Returns + ------- + + pg : dict + Mapping from parameters to lists of values for those parameters. To be + used as input for the :class:`.GenSVMGridSearchCV` class. + """ + pg = { + "p": [1.0, 1.5, 2.0], + "lmd": [1e-8, 1e-6, 1e-4, 1e-2, 1], + "kappa": [-0.9, 0.5, 5.0], + "weights": ["unit", "group"], + } + return pg + + +def load_grid_full(): + """Load the full parameter grid for GenSVM This is the parameter grid used in the GenSVM paper to run the grid search experiments. It uses a large grid for the ``lmd`` regularization parameter diff --git a/test/test_gridsearch.py b/test/test_gridsearch.py index 16f4a3f..f07e064 100644 --- a/test/test_gridsearch.py +++ b/test/test_gridsearch.py @@ -15,11 +15,7 @@ from sklearn.datasets import load_iris, load_digits from sklearn.model_selection import train_test_split from sklearn.preprocessing import maxabs_scale -from gensvm.gridsearch import ( - GenSVMGridSearchCV, - _validate_param_grid, - load_default_grid, -) +from gensvm.gridsearch import GenSVMGridSearchCV, _validate_param_grid class GenSVMGridSearchCVTestCase(unittest.TestCase): @@ -207,3 +203,39 @@ class GenSVMGridSearchCVTestCase(unittest.TestCase): } gg = GenSVMGridSearchCV(pg, verbose=True) gg.fit(Xs, ys) + + def test_gridsearch_tiny(self): + """ GENSVM_GRID: Test with tiny grid """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + clf = GenSVMGridSearchCV(param_grid="tiny") + clf.fit(X_train, y_train) + + score = clf.score(X_test, y_test) + self.assertGreaterEqual(score, 0.95) + + def test_gridsearch_small(self): + """ GENSVM_GRID: Test with small grid """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + clf = GenSVMGridSearchCV(param_grid="small") + clf.fit(X_train, y_train) + + score = clf.score(X_test, y_test) + self.assertGreaterEqual(score, 0.95) + + def test_gridsearch_full(self): + """ GENSVM_GRID: Test with full grid """ + X, y = load_iris(return_X_y=True) + X = maxabs_scale(X) + X_train, X_test, y_train, y_test = train_test_split(X, y) + + clf = GenSVMGridSearchCV(param_grid="full") + clf.fit(X_train, y_train) + + score = clf.score(X_test, y_test) + self.assertGreaterEqual(score, 0.90) |