Python metrics.precision_recall_fscore_support方法代码示例

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def accuracy(y_true, y_pred):        
    # 计算混淆矩阵
    y = np.zeros(len(y_true))
    y_ = np.zeros(len(y_true))    
    for i in range(len(y_true)): 
        y[i] = np.argmax(y_true[i,:])
        y_[i] = np.argmax(y_pred[i,:])
    cnf_mat = confusion_matrix(y, y_)
    
    # Acc = 1.0*(cnf_mat[1][1]+cnf_mat[0][0])/len(y_true)
    # Sens = 1.0*cnf_mat[1][1]/(cnf_mat[1][1]+cnf_mat[1][0])
    # Spec = 1.0*cnf_mat[0][0]/(cnf_mat[0][0]+cnf_mat[0][1])
    
    # # 绘制ROC曲线
    # fpr, tpr, thresholds = roc_curve(y_true[:,0], y_pred[:,0])
    # Auc = auc(fpr, tpr)
    
    
    # 计算多分类评价值
    Sens = recall_score(y, y_, average='macro')
    Prec = precision_score(y, y_, average='macro')
    F1 = f1_score(y, y_, average='weighted') 
    Support = precision_recall_fscore_support(y, y_, beta=0.5, average=None)
    return Sens, Prec, F1, cnf_mat 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def calc_test_result(result, test_label, test_mask):

  true_label=[]
  predicted_label=[]

  for i in range(result.shape[0]):
    for j in range(result.shape[1]):
      if test_mask[i,j]==1:
        true_label.append(np.argmax(test_label[i,j] ))
        predicted_label.append(np.argmax(result[i,j] ))
    
  print("Confusion Matrix :")
  print(confusion_matrix(true_label, predicted_label))
  print("Classification Report :")
  print(classification_report(true_label, predicted_label,digits=4))
  print("Accuracy ", accuracy_score(true_label, predicted_label))
  print("Macro Classification Report :")
  print(precision_recall_fscore_support(true_label, predicted_label,average='macro'))
  print("Weighted Classification Report :")
  print(precision_recall_fscore_support(true_label, predicted_label,average='weighted'))
  #print "Normal Classification Report :"
  #print precision_recall_fscore_support(true_label, predicted_label) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def test_fbeta_multiclass_with_weighted_average(self, device: str):
        self.predictions = self.predictions.to(device)
        self.targets = self.targets.to(device)

        labels = [0, 1]
        fbeta = FBetaMeasure(average="weighted", labels=labels)
        fbeta(self.predictions, self.targets)
        metric = fbeta.get_metric()
        precisions = metric["precision"]
        recalls = metric["recall"]
        fscores = metric["fscore"]

        weighted_precision, weighted_recall, weighted_fscore, _ = precision_recall_fscore_support(
            self.targets.cpu().numpy(),
            self.predictions.argmax(dim=1).cpu().numpy(),
            labels=labels,
            average="weighted",
        )

        # check value
        assert_allclose(precisions, weighted_precision)
        assert_allclose(recalls, weighted_recall)
        assert_allclose(fscores, weighted_fscore) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def test_precision_recall_f1_score_binary_averaged():
    y_true = np.array([0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1])
    y_pred = np.array([1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1])

    # compute scores with default labels introspection
    ps, rs, fs, _ = precision_recall_fscore_support(y_true, y_pred,
                                                    average=None)
    p, r, f, _ = precision_recall_fscore_support(y_true, y_pred,
                                                 average='macro')
    assert_equal(p, np.mean(ps))
    assert_equal(r, np.mean(rs))
    assert_equal(f, np.mean(fs))
    p, r, f, _ = precision_recall_fscore_support(y_true, y_pred,
                                                 average='weighted')
    support = np.bincount(y_true)
    assert_equal(p, np.average(ps, weights=support))
    assert_equal(r, np.average(rs, weights=support))
    assert_equal(f, np.average(fs, weights=support)) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def test_precision_recall_f1_no_labels(beta, average):
    y_true = np.zeros((20, 3))
    y_pred = np.zeros_like(y_true)

    p, r, f, s = assert_warns(UndefinedMetricWarning,
                              precision_recall_fscore_support,
                              y_true, y_pred, average=average,
                              beta=beta)
    assert_almost_equal(p, 0)
    assert_almost_equal(r, 0)
    assert_almost_equal(f, 0)
    assert_equal(s, None)

    fbeta = assert_warns(UndefinedMetricWarning, fbeta_score,
                         y_true, y_pred,
                         beta=beta, average=average)
    assert_almost_equal(fbeta, 0) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def test_precision_recall_f1_no_labels_average_none():
    y_true = np.zeros((20, 3))
    y_pred = np.zeros_like(y_true)

    beta = 1

    # tp = [0, 0, 0]
    # fn = [0, 0, 0]
    # fp = [0, 0, 0]
    # support = [0, 0, 0]
    # |y_hat_i inter y_i | = [0, 0, 0]
    # |y_i| = [0, 0, 0]
    # |y_hat_i| = [0, 0, 0]

    p, r, f, s = assert_warns(UndefinedMetricWarning,
                              precision_recall_fscore_support,
                              y_true, y_pred, average=None, beta=beta)
    assert_array_almost_equal(p, [0, 0, 0], 2)
    assert_array_almost_equal(r, [0, 0, 0], 2)
    assert_array_almost_equal(f, [0, 0, 0], 2)
    assert_array_almost_equal(s, [0, 0, 0], 2)

    fbeta = assert_warns(UndefinedMetricWarning, fbeta_score,
                         y_true, y_pred, beta=beta, average=None)
    assert_array_almost_equal(fbeta, [0, 0, 0], 2) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def print_result(fold, y, y_predicted, id_class_mapping):
    """ print result matrix """

    n_classes = len(np.unique(y))

    p, r, f, s = precision_recall_fscore_support(y, y_predicted, labels=None, pos_label=1, average=None)
    a = [(accuracy_score(y[y == c], y_predicted[y == c])) for c in xrange(n_classes)]

    # count occurrences of classes
    count = Counter(y)

    print("\n")
    if fold is not None:
        print("Results on fold %d" % fold)
    print("\n")
    print("%30s  |  %s  |  %5s  |  %4s  |  %4s  |   %4s   |" % ("LABEL", "CNT", "ACC ", "PR ", "RE ", "F1 "))
    print('-' * 70)
    for c in xrange(n_classes):
        print("%30s  |  %03d  |  %0.3f  |  %.2f  |  %.2f  |  %.3f   |" % (id_class_mapping[c], count[c], a[c], p[c], r[c], f[c]))
    print('-' * 70)
    print("%30s  |  %03d  |  %0.3f  |  %.2f  |  %.2f  |  %.3f   |" % ('average', len(y), np.mean(a), np.mean(p), np.mean(r), np.mean(f)))
    print('=' * 70)
    print("Overall Accuracy: %.3f %%" % (100.0 * accuracy_score(y, y_predicted)))
    print('=' * 70) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def _update_onco_metrics(self, y_true, y_pred, prob):
        self.onco_gene_pred = pd.Series(y_pred, self.y.index)
        self.onco_gene_score = pd.Series(prob, self.y.index)

        # compute metrics for classification
        self.onco_gene_count[self.num_pred] = sum(y_pred)
        prec, recall, fscore, support = metrics.precision_recall_fscore_support(y_true, y_pred)
        self.onco_precision[self.num_pred] = prec[self.onco_num]
        self.onco_recall[self.num_pred] = recall[self.onco_num]
        self.onco_f1_score[self.num_pred] = fscore[self.onco_num]
        self.logger.debug('Onco Iter %d: Precission=%s, Recall=%s, f1_score=%s' % (
                          self.num_pred + 1, str(prec), str(recall), str(fscore)))

        # compute ROC curve metrics
        fpr, tpr, thresholds = metrics.roc_curve(y_true, prob)
        self.onco_tpr_array[self.num_pred, :] = interp(self.onco_fpr_array, fpr, tpr)
        #self.onco_mean_tpr[0] = 0.0

        # compute Precision-Recall curve metrics
        p, r, thresh = metrics.precision_recall_curve(y_true, prob)
        p, r, thresh = p[::-1], r[::-1], thresh[::-1]  # reverse order of results
        thresh = np.insert(thresh, 0, 1.0)
        self.onco_precision_array[self.num_pred, :] = interp(self.onco_recall_array, r, p)
        self.onco_threshold_array[self.num_pred, :] = interp(self.onco_recall_array, r, thresh) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def _update_tsg_metrics(self, y_true, y_pred, prob):
        self.tsg_gene_pred = pd.Series(y_pred, self.y.index)
        self.tsg_gene_score = pd.Series(prob, self.y.index)

        # compute metrics for classification
        self.tsg_gene_count[self.num_pred] = sum(y_pred)
        prec, recall, fscore, support = metrics.precision_recall_fscore_support(y_true, y_pred)
        tsg_col = 1  # column for metrics relate to tsg
        self.tsg_precision[self.num_pred] = prec[tsg_col]
        self.tsg_recall[self.num_pred] = recall[tsg_col]
        self.tsg_f1_score[self.num_pred] = fscore[tsg_col]
        self.logger.debug('Tsg Iter %d: Precission=%s, Recall=%s, f1_score=%s' % (
                          self.num_pred + 1, str(prec), str(recall), str(fscore)))

        # compute ROC curve metrics
        fpr, tpr, thresholds = metrics.roc_curve(y_true, prob)
        self.tsg_tpr_array[self.num_pred, :] = interp(self.tsg_fpr_array, fpr, tpr)
        #self.tsg_tpr_array[0] = 0.0

        # compute Precision-Recall curve metrics
        p, r, thresh = metrics.precision_recall_curve(y_true, prob)
        p, r, thresh = p[::-1], r[::-1], thresh[::-1]  # reverse order of results
        self.tsg_precision_array[self.num_pred, :] = interp(self.tsg_recall_array, r, p) 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def metrics(y_pred, y_true):
    """ Calucate evaluation metrics for precision, recall, and f1.

    Arguments
    ---------
        y_pred: ndarry, the predicted result list
        y_true: ndarray, the ground truth label list

    Returns
    -------
        precision: float, precision value
        recall: float, recall value
        f1: float, f1 measure value
    """
    precision, recall, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary')
    return precision, recall, f1 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def validation(classifier, data, y_data, y_target, class_names, title):
    #kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
    #cv = kfold
    t = 'Confusion matrix: '+str(title)
    x =  np.transpose(data)
    if (classifier == None):
        print ("No accuracy to be computed")
    else:
        accuracy = model_selection.cross_val_score(classifier, x, y_target, scoring='accuracy')
        print("Accuracy: "+ str(accuracy))
    #precision = model_selection.cross_val_score(self.classifier, x, target, scoring='precision')
    #precision_score(y_true, y_pred, average='macro')  
    #recall = model_selection.cross_val_score(self.classifier, x, target, scoring='recall')
    precision, recall, fscore, m = precision_recall_fscore_support(y_target, y_data, average='macro')
    cnf_matrix = confusion_matrix(y_target, y_data)
    print("Precision: " +str(precision) +", Recall:" +str(recall) + ", f-score:" +str(fscore))

    np.set_printoptions(precision=2)
    # Plot non-normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(cnf_matrix, classes=class_names, title=t)
    print ("... finishing matrix plot")
    plt.show() 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def evaluate(model, data_iterator, num_steps, metric_labels):
    """Evaluate the model on `num_steps` batches."""
    # set model to evaluation mode
    model.eval()

    output_labels = list()
    target_labels = list()

    # compute metrics over the dataset
    for _ in range(num_steps):
        # fetch the next evaluation batch
        batch_data, batch_labels = next(data_iterator)
        
        # compute model output
        batch_output = model(batch_data)  # batch_size x num_labels
        batch_output_labels = torch.max(batch_output, dim=1)[1]
        output_labels.extend(batch_output_labels.data.cpu().numpy().tolist())
        target_labels.extend(batch_labels.data.cpu().numpy().tolist())

    # Calculate precision, recall and F1 for all relation categories
    p_r_f1_s = precision_recall_fscore_support(target_labels, output_labels, labels=metric_labels, average='micro')
    p_r_f1 = {'precison': p_r_f1_s[0] * 100,
              'recall': p_r_f1_s[1] * 100,
              'f1': p_r_f1_s[2] * 100}
    return p_r_f1 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def summary_util(self, type):
        if type == "test":
            Y_hat = self.model.predict(self.X_test)
            Y = self.Y_test
        elif type == "train":
            Y_hat = self.model.predict(self.X_train) 
            Y = self.Y_train
        elif type == "val":
            Y_hat = self.model.predict(self.X_val)
            Y = self.Y_val
        elif type == "forecast":
            Y_hat = self.model.predict(self.X_forecast) 
            Y = self.Y_forecast

        Y_pred = Y_hat  > 0.5

        precision,recall,F1,junk = precision_recall_fscore_support(Y,Y_pred)
        out = dict()
        out['precision']=precision[1]
        out['recall']=recall[1]
        out['F1']=F1[1]

        return out 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def boot_human(i, sample_size=sample_size):
    np.random.seed(seed=i)
    random_pids = np.random.choice(pt_list_unique_sub, size=sample_size, replace=True)
    test = np.array([p_2_id_sub[pid] for pid in random_pids])
    boot_list = []
    for ids in test:
        size = len(ids)
        boot_list.append(np.random.choice(ids))    
    y_pred_sub = human_authored[boot_list, :]
    y_true_sub = subset_y[boot_list, :]
    # evaluate model
#     print('calculating')

    
    output = precision_recall_fscore_support(y_true_sub.flatten(), y_pred_sub.flatten())
    precision = output[0][2]
    recall = output[1][2]
    f1 = output[2][2]

#     print('done')
    return precision, recall, f1 

# 需要导入模块: from sklearn import metrics [as 别名]
# 或者: from sklearn.metrics import precision_recall_fscore_support [as 别名]
def boot_human_clinic(i, sample_size=sample_size):
    np.random.seed(seed=i)
    random_pids = np.random.choice(pt_list_unique_sub, size=sample_size, replace=True)
    test = np.array([p_2_id_sub[pid] for pid in random_pids])
    boot_list = []
    for ids in test:
        size = len(ids)
        boot_list.append(np.random.choice(ids))   
    y_pred_sub = subset_ClinicNet[boot_list, :]
    y_true_sub = subset_y[boot_list, :]
    auroc = roc_auc_score(y_true_sub, y_pred_sub, average='micro')
    avg_precision = average_precision_score(y_true_sub, y_pred_sub, average='micro')
    y_pred_sub[y_pred_sub<threshold_clinicnet] = 0
    y_pred_sub[y_pred_sub>=threshold_clinicnet] = 1
    # evaluate model
#     print('calculating')
    output = precision_recall_fscore_support(y_true_sub.flatten(), y_pred_sub.flatten())
#     print('done')
    precision = output[0][1]
    recall = output[1][1]
    f1 = output[2][1]
#     print('done')
    return auroc, avg_precision, precision, recall, f1