def score(self, Kyx, cy): if self.scenario == 'multiclass': cy = tuple_labels_to_list_labels(cy) return self._score_one_vs_one(Kyx, cy) elif self.scenario == 'versus_null': cy = tuple_labels_to_list_labels(cy) return self._score_one_vs_rest(Kyx, cy) elif self.scenario == 'versus_null_sklearn': cy = tuple_labels_to_list_labels(cy) return self._score_one_vs_rest_sklearn(Kyx, cy)
def score(self, Kyx, cy): if self.scenario == "multiclass": cy = tuple_labels_to_list_labels(cy) return self._score_one_vs_one(Kyx, cy) elif self.scenario == "versus_null": cy = tuple_labels_to_list_labels(cy) return self._score_one_vs_rest(Kyx, cy) elif self.scenario == "versus_null_sklearn": cy = tuple_labels_to_list_labels(cy) return self._score_one_vs_rest_sklearn(Kyx, cy)
def fit(self, Kxx, cx): if self.scenario == "multiclass": cx = tuple_labels_to_list_labels(cx) self._fit_one_vs_one(Kxx, cx) elif self.scenario == "versus_null": cx = tuple_labels_to_list_labels(cx) self._fit_one_vs_rest(Kxx, cx) elif self.scenario == "versus_null_sklearn": cx = tuple_labels_to_list_labels(cx) self._fit_one_vs_rest_sklearn(Kxx, cx) return self
def fit(self, Kxx, cx): if self.scenario == 'multiclass': cx = tuple_labels_to_list_labels(cx) self._fit_one_vs_one(Kxx, cx) elif self.scenario == 'versus_null': cx = tuple_labels_to_list_labels(cx) self._fit_one_vs_rest(Kxx, cx) elif self.scenario == 'versus_null_sklearn': cx = tuple_labels_to_list_labels(cx) self._fit_one_vs_rest_sklearn(Kxx, cx) return self
def fit(self, Kxx, cx): """ Fits SVM to kernel matrix and cross-validate the regularization weight across a logarithmic scale. """ cx = tuple_labels_to_list_labels(cx) my_svm = svm.SVC(kernel="precomputed") c_values = np.power(3.0, np.arange(-2, 8)) tuned_parameters = [{"C": c_values}] splits = StratifiedShuffleSplit(cx, 3, test_size=0.3) self.clf = GridSearchCV(my_svm, tuned_parameters, score_func=zero_one_score, cv=splits, n_jobs=4) self.clf.fit(Kxx, cx) return self
def fit(self, Kxx, cx): """ Fits SVM to kernel matrix and cross-validate the regularization weight across a logarithmic scale. """ cx = tuple_labels_to_list_labels(cx) my_svm = svm.SVC(kernel='precomputed') c_values = np.power(3.0, np.arange(-2, 8)) tuned_parameters = [{'C': c_values}] splits = StratifiedShuffleSplit(cx, 3, test_size=0.3) self.clf = GridSearchCV(my_svm, tuned_parameters, score_func=zero_one_score, cv=splits, n_jobs=4) self.clf.fit(Kxx, cx) return self
def score(self, Kyx, cy): """ Return the accuracy score / zero-one score. """ cy = tuple_labels_to_list_labels(cy) return self.clf.score(Kyx, cy) * 100
def predict(self, Kyx, cy): cy = tuple_labels_to_list_labels(cy) if self.scenario == "multiclass": return self._predict_one_vs_one(Kyx, cy) return None
def predict(self, Kyx, cy): cy = tuple_labels_to_list_labels(cy) if self.scenario == 'multiclass': return self._predict_one_vs_one(Kyx, cy) return None