10 examples of 'accuracy_score sklearn' in Python

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52def 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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26def accuracy_score(self, x_test, y_test):
27 estimation = self.model.predict(x_test)
28 return acc_func(estimation,y_test)
7def accuracy(params):
8 clf = RandomForestClassifier(**params)
9 clf.fit(x_train,y_train)
10 return clf.score(x_test, y_test)
114def 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)
613def 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))
29def testAccuracy(data_test, label_test, kNN):
30 return kNN.score(data_test, label_test)
53def 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()
57def 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()
426def 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 )
272def 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

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