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52 def test_accuracy(clf, X_test, y_test): 53 X_test = np.array([item for sublist in X_test for item in sublist]) 54 y_test = np.array([item for sublist in y_test for item in sublist]) 55 return clf.score(X_test, y_test)

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26 def accuracy_score(self, x_test, y_test): 27 estimation = self.model.predict(x_test) 28 return acc_func(estimation,y_test)

7 def accuracy(params): 8 clf = RandomForestClassifier(**params) 9 clf.fit(x_train,y_train) 10 return clf.score(x_test, y_test)

114 def accuracy(y_test, y_pred): 115 """Computes the accuracy score. 116 117 Args: 118 y_test: np.array 1-D array of true class labels 119 y_pred: np.array 1-D array of predicted class labels 120 121 Returns: 122 accuracy: float 123 accuracy score 124 """ 125 return metrics.accuracy_score(y_test, y_pred)

613 def score(self, X, y): 614 """Force use of accuracy score since we don't inherit 615 from ClassifierMixin""" 616 617 from sklearn.metrics import accuracy_score 618 return accuracy_score(y, self.predict(X))

29 def testAccuracy(data_test, label_test, kNN): 30 return kNN.score(data_test, label_test)

53 def train_and_score(X, y): 54 X_train, X_test, y_train, y_test = split_data(X, y) 55 56 clf = Pipeline([ 57 ('reduce_dim', SelectKBest(chi2, k=2)), 58 ('train', LinearSVC(C=100)) 59 ]) 60 61 scores = cross_val_score(clf, X_train, y_train, cv=5, n_jobs=2) 62 print("Mean Model Accuracy:", np.array(scores).mean()) 63 64 clf.fit(X_train, y_train) 65 66 confuse(y_test, clf.predict(X_test)) 67 print()

57 def accuracy(self, **kwargs): 58 """ 59 It measures how many observations, both positive and negative, were correctly classified. 60 61 Returns 62 ------- 63 float 64 Accuracy 65 66 Examples 67 -------- 68 >>> m = model.LogisticRegression() 69 >>> m.accuracy() 70 """ 71 72 return metrics.accuracy_score(self.y_test, self.y_pred, **kwargs)

425 @torch.no_grad() 426 def compute_accuracy_classifier(clf, data_train, labels_train, data_test, labels_test): 427 clf.fit(data_train, labels_train) 428 # Predicting the labels 429 y_pred_test = clf.predict(data_test) 430 y_pred_train = clf.predict(data_train) 431 432 return ( 433 ( 434 compute_accuracy_tuple(labels_train, y_pred_train), 435 compute_accuracy_tuple(labels_test, y_pred_test), 436 ), 437 y_pred_test, 438 )

272 def cluster_acc(y_true, y_pred): 273 """ 274 calculating the accuracy of the clustering. 275 since the index of each cluster might be different in y_true and y_pred, this function finds the linear 276 assignment which maximizes the accuracy. This means some of the clusters might remain without a matching label. 277 :param y_true: ground truth labeling 278 :param y_pred: calculated from the model 279 :return: the accuracy percentage, ami, nmi and the matrix w of all the combinations of indexes of the original clusters 280 and the calculated ones 281 """ 282 assert y_pred.size == y_true.size 283 y_true_unique = np.unique(y_true) 284 true_cluster_idx = np.nonzero(y_true[:, None] == y_true_unique)[1] 285 D = max(y_pred.max()+1, len(y_true_unique)) # number of clusters 286 w = np.zeros((D, len(y_true_unique)), dtype=np.int64) # D is in size number of clusters*number of clusters 287 for i in range(y_pred.size): 288 w[y_pred[i], true_cluster_idx[i]] += 1 289 ind = linear_assignment(w.max() - w) 290 # calculating the corresponding gt label most fit for each y_pred. since there are usually a lot of clusters, 291 # the ones which didn't correspond to a value in the gt will receive the value -1 292 y_pred_new = -1 * np.ones(len(y_pred), int) 293 for i in range(0, len(y_pred)): 294 j = np.argwhere(ind[:, 0] == y_pred[i]) 295 if j.shape[0] > 0: 296 y_pred_new[i] = (ind[j[0], 1]) 297 acc = sum([w[i, j] for i, j in ind])*1.0/y_pred.size 298 ami = adjusted_mutual_info_score(y_true, y_pred) 299 nmi = normalized_mutual_info_score(y_true, y_pred) 300 return acc, ami, nmi, w, y_pred_new