Every line of 'sklearn metrics accuracy' code snippets is scanned for vulnerabilities by our powerful machine learning engine that combs millions of open source libraries, ensuring your Python code is secure.

By copying the Snyk Code Snippets you agree to

this disclaimer

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)

Important

Secure your code as it's written. Use Snyk Code to scan source code in minutes – no build needed – and fix issues immediately. Enable Snyk Code

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)

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

29 @unhot 30 def accuracy(actual, predicted): 31 return 1.0 - classification_error(actual, predicted)

37 def accuracy(y_test, y_pred): 38 return np.sum(y_test==y_pred)/len(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)

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

207 def accuracy(score_vec, label_vec, thresholds=None): 208 assert len(score_vec.shape)==1 209 assert len(label_vec.shape)==1 210 assert score_vec.shape == label_vec.shape 211 assert label_vec.dtype==np.bool 212 # find thresholds by TAR 213 if thresholds is None: 214 score_pos = score_vec[label_vec==True] 215 thresholds = np.sort(score_pos)[::1] 216 217 assert len(thresholds.shape)==1 218 if np.size(thresholds) > 10000: 219 warn('number of thresholds (%d) very large, computation may take a long time!' % np.size(thresholds)) 220 221 # Loop Computation 222 accuracies = np.zeros(np.size(thresholds)) 223 for i, threshold in enumerate(thresholds): 224 pred_vec = score_vec>=threshold 225 accuracies[i] = np.mean(pred_vec==label_vec) 226 227 # Matrix Computation, Each column is a threshold 228 # predictions = score_vec[:,None] >= thresholds[None,:] 229 # accuracies = np.mean(predictions==label_vec[:,None], axis=0) 230 231 argmax = np.argmax(accuracies) 232 accuracy = accuracies[argmax] 233 threshold = np.mean(thresholds[accuracies==accuracy]) 234 235 return accuracy, threshold

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

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 )