diff options
Diffstat (limited to 'gensvm/gridsearch.py')
| -rw-r--r-- | gensvm/gridsearch.py | 251 |
1 files changed, 150 insertions, 101 deletions
diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py index d5ea31e..dc835f9 100644 --- a/gensvm/gridsearch.py +++ b/gensvm/gridsearch.py @@ -27,29 +27,33 @@ from sklearn.utils.validation import indexable from .cython_wrapper import wrapper from .core import GenSVM -from .sklearn_util import (_skl_format_cv_results, _skl_check_scorers, - _skl_check_is_fitted, _skl_grid_score) +from .sklearn_util import ( + _skl_format_cv_results, + _skl_check_scorers, + _skl_check_is_fitted, + _skl_grid_score, +) def _sort_candidate_params(candidate_params): - if any(('epsilon' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('epsilon'), reverse=True) - if any(('p' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('p')) - if any(('lmd' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('lmd')) - if any(('kappa' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('kappa')) - if any(('weights' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('weights')) - if any(('gamma' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('gamma')) - if any(('degree' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('degree')) - if any(('coef' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('coef')) - if any(('kernel' in p for p in candidate_params)): - candidate_params.sort(key=itemgetter('kernel')) + if any(("epsilon" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("epsilon"), reverse=True) + if any(("p" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("p")) + if any(("lmd" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("lmd")) + if any(("kappa" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("kappa")) + if any(("weights" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("weights")) + if any(("gamma" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("gamma")) + if any(("degree" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("degree")) + if any(("coef" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("coef")) + if any(("kernel" in p for p in candidate_params)): + candidate_params.sort(key=itemgetter("kernel")) def _validate_param_grid(param_grid): @@ -61,32 +65,32 @@ def _validate_param_grid(param_grid): """ # the conditions that the parameters must satisfy conditions = { - 'p': lambda x : 1.0 <= x <= 2.0, - 'kappa': lambda x : x > -1.0, - 'lmd': lambda x : x > 0, - 'epsilon': lambda x : x > 0, - 'gamma' : lambda x : x != 0, - 'weights' : lambda x : x in ['unit', 'group'], - } + "p": lambda x: 1.0 <= x <= 2.0, + "kappa": lambda x: x > -1.0, + "lmd": lambda x: x > 0, + "epsilon": lambda x: x > 0, + "gamma": lambda x: x != 0, + "weights": lambda x: x in ["unit", "group"], + } for param in conditions: if param in param_grid: if not all(map(conditions[param], param_grid[param])): raise ValueError( - "Invalid value in grid for parameter: %s." % (param) - ) + "Invalid value in grid for parameter: %s." % (param) + ) class _MockEstimator(ClassifierMixin): - #This mock estimator facilitates the use of the Scorer class of - #Scikit-Learn. Basically, we want to use the _score function of - #sklearn.model_selection._validation, but we don't keep track of the - #individual estimators in the GenSVM C grid search code. With this wrapper - #we can mock an estimator for the _score function. + # This mock estimator facilitates the use of the Scorer class of + # Scikit-Learn. Basically, we want to use the _score function of + # sklearn.model_selection._validation, but we don't keep track of the + # individual estimators in the GenSVM C grid search code. With this wrapper + # we can mock an estimator for the _score function. - #The ClassifierMixin adds the score method to the estimator. This allows us - #to leave scoring=None as the default to the GenSVMGridSearchCV class and - #ends up using the accuracy_score metric. + # The ClassifierMixin adds the score method to the estimator. This allows us + # to leave scoring=None as the default to the GenSVMGridSearchCV class and + # ends up using the accuracy_score metric. def __init__(self, predictions): self.predictions = predictions @@ -95,11 +99,17 @@ class _MockEstimator(ClassifierMixin): return self.predictions -def _format_results(results, cv_idx, true_y, scorers, iid, - return_train_score=True, - return_n_test_samples=True, - return_times=True, - return_parameters=False): +def _format_results( + results, + cv_idx, + true_y, + scorers, + iid, + return_train_score=True, + return_n_test_samples=True, + return_times=True, + return_parameters=False, +): """Format the results from the grid search Parameters @@ -118,49 +128,52 @@ def _format_results(results, cv_idx, true_y, scorers, iid, """ out = [] - candidate_params = results['params'] + candidate_params = results["params"] n_candidates = len(candidate_params) n_splits = len(np.unique(cv_idx)) is_multimetric = not callable(scorers) - # Out must be a list of dicts of size n_params x n_splits that iterates + # Out must be a list of dicts of size n_params x n_splits that iterates # over the params in the list and for each param iterates over the splits. - for param, duration, predictions in zip(results['params'], - results['duration'], results['predictions']): + for param, duration, predictions in zip( + results["params"], results["duration"], results["predictions"] + ): for test_idx in np.unique(cv_idx): ret = [] score_time = 0 if return_train_score: - train_pred = predictions[cv_idx != test_idx, ] - y_train = true_y[cv_idx != test_idx, ] + train_pred = predictions[cv_idx != test_idx,] + y_train = true_y[cv_idx != test_idx,] train_mock = _MockEstimator(train_pred) start_time = time.time() - train_scores = _score(train_mock, None, y_train, scorers, - is_multimetric) + train_scores = _score( + train_mock, None, y_train, scorers, is_multimetric + ) score_time += time.time() - start_time ret.append(train_scores) - test_pred = predictions[cv_idx == test_idx, ] - y_test = true_y[cv_idx == test_idx, ] + test_pred = predictions[cv_idx == test_idx,] + y_test = true_y[cv_idx == test_idx,] test_mock = _MockEstimator(test_pred) start_time = time.time() - test_scores = _score(test_mock, None, y_test, scorers, - is_multimetric) + test_scores = _score( + test_mock, None, y_test, scorers, is_multimetric + ) score_time += time.time() - start_time ret.append(test_scores) if return_n_test_samples: ret.append(len(y_test)) if return_times: - # Note, the C library returns the duration for a task (i.e. all - # splits). The _skkl_format_cv_results() computes the mean of - # the values, which should represent the average time per - # split. To compute this correctly, we here divide by the - # number of splits. Since we calculate the mean later, the mean - # is still correct, but this is not the exact fit_time for this + # Note, the C library returns the duration for a task (i.e. all + # splits). The _skkl_format_cv_results() computes the mean of + # the values, which should represent the average time per + # split. To compute this correctly, we here divide by the + # number of splits. Since we calculate the mean later, the mean + # is still correct, but this is not the exact fit_time for this # fold. fit_time = duration / n_splits ret.extend([fit_time, score_time]) @@ -169,14 +182,31 @@ def _format_results(results, cv_idx, true_y, scorers, iid, out.append(ret) - cv_results_ = _skl_format_cv_results(out, return_train_score, - candidate_params, n_candidates, n_splits, scorers, iid) + cv_results_ = _skl_format_cv_results( + out, + return_train_score, + candidate_params, + n_candidates, + n_splits, + scorers, + iid, + ) return cv_results_ -def _fit_grid_gensvm(X, y, groups, candidate_params, scorers, cv, refit, - verbose, return_train_score, iid): +def _fit_grid_gensvm( + X, + y, + groups, + candidate_params, + scorers, + cv, + refit, + verbose, + return_train_score, + iid, +): """Utility function for fitting the grid search for GenSVM This function sorts the parameter grid for optimal computation speed, sets @@ -193,35 +223,30 @@ def _fit_grid_gensvm(X, y, groups, candidate_params, scorers, cv, refit, """ # sort the candidate params - # the optimal order of the parameters from inner to outer loop is: epsilon, + # the optimal order of the parameters from inner to outer loop is: epsilon, # p, lambda, kappa, weights, kernel, ??? _sort_candidate_params(candidate_params) # set the verbosity in GenSVM wrapper.set_verbosity_wrap(verbose) - # NOTE: The C library can compute the accuracy score and destroy the exact - # predictions, but this doesn't allow us to compute the score per fold. So + # NOTE: The C library can compute the accuracy score and destroy the exact + # predictions, but this doesn't allow us to compute the score per fold. So # we always want to get the raw predictions for each grid point. store_predictions = True # Convert the cv variable to a cv_idx array cv = check_cv(cv, y, classifier=True) n_folds = cv.get_n_splits(X, y, groups) - cv_idx = np.zeros((X.shape[0], ), dtype=np.int_) - 1 + cv_idx = np.zeros((X.shape[0],), dtype=np.int_) - 1 fold_idx = 0 for train, test in cv.split(X, y, groups): - cv_idx[test, ] = fold_idx + cv_idx[test,] = fold_idx fold_idx += 1 results_ = wrapper.grid_wrap( - X, - y, - candidate_params, - int(store_predictions), - cv_idx, - int(n_folds), - ) + X, y, candidate_params, int(store_predictions), cv_idx, int(n_folds) + ) cv_results_ = _format_results(results_, cv_idx, y, scorers, iid) return cv_results_, n_folds @@ -449,8 +474,16 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html """ - def __init__(self, param_grid, scoring=None, iid=True, cv=None, refit=True, - verbose=0, return_train_score=True): + def __init__( + self, + param_grid, + scoring=None, + iid=True, + cv=None, + refit=True, + verbose=0, + return_train_score=True, + ): self.param_grid = param_grid _check_param_grid(self.param_grid) @@ -466,7 +499,6 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): def _get_param_iterator(self): return ParameterGrid(self.param_grid) - def fit(self, X, y, groups=None): """Run GenSVM grid search with all sets of parameters @@ -491,14 +523,15 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): """ - X, y_orig = check_X_y(X, y, accept_sparse=False, dtype=np.float64, - order="C") + X, y_orig = check_X_y( + X, y, accept_sparse=False, dtype=np.float64, order="C" + ) y_type = type_of_target(y_orig) if y_type not in ["binary", "multiclass"]: raise ValueError("Label type not allowed for GenSVM: %r" % y_type) - # This is necessary because GenSVM expects classes to go from 1 to + # This is necessary because GenSVM expects classes to go from 1 to # n_class self.encoder = LabelEncoder() y = self.encoder.fit_transform(y_orig) @@ -507,13 +540,23 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): candidate_params = list(self._get_param_iterator()) scorers, self.multimetric_, refit_metric = _skl_check_scorers( - self.scoring, self.refit) + self.scoring, self.refit + ) X, y, groups = indexable(X, y, groups) - results, n_splits = _fit_grid_gensvm(X, y, groups, candidate_params, - scorers, self.cv, self.refit, self.verbose, - self.return_train_score, self.iid) + results, n_splits = _fit_grid_gensvm( + X, + y, + groups, + candidate_params, + scorers, + self.cv, + self.refit, + self.verbose, + self.return_train_score, + self.iid, + ) self.cv_results_ = results @@ -524,23 +567,23 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): self.best_index_ = results["rank_test_%s" % refit_metric].argmin() self.best_params_ = candidate_params[self.best_index_] self.best_score_ = results["mean_test_%s" % refit_metric][ - self.best_index_] + self.best_index_ + ] if self.refit: self.best_estimator_ = GenSVM(**self.best_params_) - # y_orig because GenSVM fit must know the conversion for predict to + # y_orig because GenSVM fit must know the conversion for predict to # work correctly self.best_estimator_.fit(X, y_orig) ## Store the only scorer not as a dict for single metric evaluation - self.scorer_ = scorers if self.multimetric_ else scorers['score'] + self.scorer_ = scorers if self.multimetric_ else scorers["score"] self.cv_results_ = results self.n_splits_ = n_splits return self - def score(self, X, y): """Compute the score on the test data given the true labels @@ -559,9 +602,15 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): score : float """ - _skl_check_is_fitted(self, 'score', self.refit) - return _skl_grid_score(X, y, self.scorer_, self.best_estimator_, - self.refit, self.multimetric_) + _skl_check_is_fitted(self, "score", self.refit) + return _skl_grid_score( + X, + y, + self.scorer_, + self.best_estimator_, + self.refit, + self.multimetric_, + ) def predict(self, X): """Predict the class labels on the test data @@ -578,7 +627,7 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin): Predicted class labels of the data in X. """ - _skl_check_is_fitted(self, 'predict', self.refit) + _skl_check_is_fitted(self, "predict", self.refit) return self.best_estimator_.predict(X) @@ -609,11 +658,11 @@ def load_default_grid(): used as input for the :class:`.GenSVMGridSearchCV` class. """ pg = { - 'lmd': [pow(2, x) for x in range(-18, 19, 2)], - 'kappa': [-0.9, 0.5, 5.0], - 'p': [1.0, 1.5, 2.0], - 'weights': ['unit', 'group'], - 'epsilon': [1e-8], - 'kernel': ['linear'] - } + "lmd": [pow(2, x) for x in range(-18, 19, 2)], + "kappa": [-0.9, 0.5, 5.0], + "p": [1.0, 1.5, 2.0], + "weights": ["unit", "group"], + "epsilon": [1e-8], + "kernel": ["linear"], + } return pg |