Python sklearn.metrics.classification_report() Examples

def multi_class_classification(data_X,data_Y):
    '''
    calculate multi-class classification and return related evaluation metrics
    '''

    svc = svm.SVC(C=1, kernel='linear')
    # X_train, X_test, y_train, y_test = train_test_split( data_X, data_Y, test_size=0.4, random_state=0) 
    clf = svc.fit(data_X, data_Y) #svm
    # array = svc.coef_
    # print array
    predicted = cross_val_predict(clf, data_X, data_Y, cv=2)
    print "accuracy",metrics.accuracy_score(data_Y, predicted)
    print "f1 score macro",metrics.f1_score(data_Y, predicted, average='macro') 
    print "f1 score micro",metrics.f1_score(data_Y, predicted, average='micro') 
    print "precision score",metrics.precision_score(data_Y, predicted, average='macro') 
    print "recall score",metrics.recall_score(data_Y, predicted, average='macro') 
    print "hamming_loss",metrics.hamming_loss(data_Y, predicted)
    print "classification_report", metrics.classification_report(data_Y, predicted)
    print "jaccard_similarity_score", metrics.jaccard_similarity_score(data_Y, predicted)
    # print "log_loss", metrics.log_loss(data_Y, predicted)
    print "zero_one_loss", metrics.zero_one_loss(data_Y, predicted)
    # print "AUC&ROC",metrics.roc_auc_score(data_Y, predicted)
    # print "matthews_corrcoef", metrics.matthews_corrcoef(data_Y, predicted) 

def evaluation_analysis(true_label,predicted): 
    '''
    return all metrics results
    '''
    print "accuracy",metrics.accuracy_score(true_label, predicted)
    print "f1 score macro",metrics.f1_score(true_label, predicted, average='macro')     
    print "f1 score micro",metrics.f1_score(true_label, predicted, average='micro') 
    print "precision score",metrics.precision_score(true_label, predicted, average='macro') 
    print "recall score",metrics.recall_score(true_label, predicted, average='macro') 
    print "hamming_loss",metrics.hamming_loss(true_label, predicted)
    print "classification_report", metrics.classification_report(true_label, predicted)
    print "jaccard_similarity_score", metrics.jaccard_similarity_score(true_label, predicted)
    print "log_loss", metrics.log_loss(true_label, predicted)
    print "zero_one_loss", metrics.zero_one_loss(true_label, predicted)
    print "AUC&ROC",metrics.roc_auc_score(true_label, predicted)
    print "matthews_corrcoef", metrics.matthews_corrcoef(true_label, predicted) 

def learn_structure(self, samples):
        X_train, X_train_label, X_test, X_test_label = \
            self._generate_train_test_sets(samples, 0.75)
        logger.info('Training with ' + str(len(X_train)) +
                    'samples; testing with ' + str(len(X_test)) + ' samples.')

        svc_detector = self._get_best_detector(X_train, X_train_label)
        Y_test = svc_detector.predict(X_test)

        num_anomalies = Y_test[Y_test == ANOMALY].size
        logger.info('Found ' + str(num_anomalies) +
                    ' anomalies in testing set')

        logger.info('Confusion Matrix: \n{}'.
                    format(classification_report(
                        X_test_label,
                        Y_test,
                        target_names=['no', 'yes'])))
        return svc_detector 

def score_binary_classification(y, y_hat, report=True):
    """
    Create binary classification output
    :param y: true value
    :param y_hat: class 1 probabilities
    :param report:
    :return:
    """
    y_hat_class = [1 if x >= 0.5 else 0 for x in y_hat]  # convert probability to class for classification report

    report_string = "---Binary Classification Score--- \n"
    report_string += classification_report(y, y_hat_class)
    score = roc_auc_score(y, y_hat)
    report_string += "\nAUC = " + str(score)

    if report:
        print(report_string)

    return score, report_string 

def skl_knn(self):
        """k: number of neighbors to use in classification
        test_data: the data/targets used to test the classifier
        stored_data: the data/targets used to classify the test_data
        """
        fifty_x, fifty_y = self.mk_dataset(50000)
        test_img = [self.data[i] for i in self.indx[60000:70000]]
        test_img1 = np.array(test_img)
        test_target = [self.target[i] for i in self.indx[60000:70000]]
        test_target1 = np.array(test_target)
        self.classifier.fit(fifty_x, fifty_y)

        y_pred = self.classifier.predict(test_img1)
        pickle.dump(self.classifier, open('knn.sav', 'wb'))
        print(classification_report(test_target1, y_pred))
        print("KNN Classifier model saved as knn.sav!") 

def bio_classification_report(y_true, y_pred):
    """
    Classification report for a l ist of BIOSE-encoded sequences.
    It computes token-level metrics and discards 'O' labels.
    :param y_true:
    :param y_pred:
    :return:
    """
    lb = LabelBinarizer()
    y_true_combined = lb.fit_transform(y_true)
    y_pred_combined = lb.transform(y_pred)

    tagset = set(lb.classes_) - {'O'}
    tagset = set(lb.classes_)
    tagset = sorted(tagset, key=lambda tag: tag.split('-', 1)[::-1])
    class_indices = {
        cls: idx for idx, cls in enumerate(lb.classes_)
    }

    return classification_report(
        y_true_combined,
        y_pred_combined,
        labels=[class_indices[cls] for cls in tagset],
        target_names=tagset
    ) 

def print_metric(self, y_true, y_pred, mode):

        if mode in ["train", "test"]:
            print(mode)
            if (self.config.data == "dailydialog"):
                print(classification_report(y_true, y_pred, labels=[1,2,3,4,5,6], digits=4))
            else:
                print(classification_report(y_true, y_pred, digits=4))
        

        if (self.config.data == "dailydialog"):
            weighted_fscore = classification_report(y_true, y_pred, labels=[1,2,3,4,5,6], output_dict=True, digits=4)["weighted avg"]["f1-score"]
        else:
            weighted_fscore = classification_report(y_true, y_pred, output_dict=True, digits=4)["weighted avg"]["f1-score"]

        return weighted_fscore 

def report(y_true: List[int], y_pred: List[int], labels: List[int],
           class_name: List[str] = None, report_name: str = "Default Name") -> Tuple:
    print("{} Result Reporting:".format(report_name))

    if class_name is None:
        class_name = ["class {}".format(i) for i in labels]

    assert len(class_name) == len(labels)

    cmatrix = confusion_matrix(y_true, y_pred, labels=labels)

    print("Sklearn Confusion Matrix:")
    print(cmatrix)
    print(classification_report(y_true, y_pred, target_names=class_name, digits=5))

    RkCC = compute_RkCC(cmatrix)
    print('Rk correlation coefficient = %.4f' % RkCC)

    return cmatrix, RkCC 

def eval_batch_col(classifier, val_dataset, batch_size, device):

    val_batch_generator = datasets.generate_batches_col(val_dataset,
                                               batch_size=batch_size,
                                               shuffle=False,
                                               drop_last=True,
                                               device=device)

    y_pred, y_true = [], []
    for batch_idx, batch_dict in enumerate(val_batch_generator):
        y = batch_dict["label"]
        X = batch_dict["data"]

        # Pred
        pred = classifier(X)
        y_pred.extend(pred.cpu().numpy())
        y_true.extend(y.cpu().numpy())

    
    report = classification_report(y_true, np.argmax(y_pred, axis=1), output_dict=True)
    return report

# evaluate and return prediction & true labels of a table batch 

def train_and_evaluate(clf, X_train, X_test, y_train, y_test):
    clf.fit(X_train, y_train)
    print ("Accuracy on training set:")
    print (clf.score(X_train, y_train))
    print ("Accuracy on testing set:")
    print (clf.score(X_test, y_test))
    y_pred = clf.predict(X_test)
    print ("Classification Report:")
    print (metrics.classification_report(y_test, y_pred))
    print ("Confusion Matrix:")
    print (metrics.confusion_matrix(y_test, y_pred))


# ===============================================================================
# from FaceDetectPredict.py
# =============================================================================== 

def eval(self, test_x, test_y, crf_model):
        tagger = pycrfsuite.Tagger()
        tagger.open(crf_model)

        y_pred = []
        for feat_list in test_x:
            preds = tagger.tag(feat_list)
            y_pred.append(preds)

        lb = LabelBinarizer()
        y_true_all = lb.fit_transform(list(chain.from_iterable(test_y)))
        y_pred_all = lb.transform(list(chain.from_iterable(y_pred)))

        tagset = sorted(set(lb.classes_))
        class_indices = {cls: idx for idx, cls in enumerate(lb.classes_)}

        print(classification_report(
            y_true_all,
            y_pred_all,
            labels=[class_indices[cls] for cls in tagset],
            target_names=tagset,
            digits=5
        )) 

def score_multiclass_classification(y, y_hat, report=True):
    """
    Create multiclass classification score
    :param y:
    :param y_hat:
    :return:
    """
    report_string = "---Multiclass Classification Score--- \n"
    report_string += classification_report(y, y_hat)
    score = accuracy_score(y, y_hat)
    report_string += "\nAccuracy = " + str(score)

    if report:
        print(report_string)

    return score, report_string 

def print_evaluation(model,data,ls,log=None):
    features,actual = data
    predictions = predict(model, features, 500).data.numpy().reshape(-1).tolist()

    labels = [ls.idx[i] for i, _ in enumerate(ls.idx)]

    actual = [labels[i] for i in actual]
    predictions = [labels[i] for i in predictions]

    print(accuracy_score(actual, predictions))
    print(classification_report(actual, predictions))
    print(confusion_matrix(actual, predictions))

    data = zip(actual,predictions)
    if log is not None:
        f = open(log, "w+")
        for a,p in data:
            f.write(json.dumps({"actual": a, "predicted": p}) + "\n")
        f.close() 

def test_classification_report_multiclass_with_unicode_label():
    y_true, y_pred, _ = make_prediction(binary=False)

    labels = np.array(["blue\xa2", "green\xa2", "red\xa2"])
    y_true = labels[y_true]
    y_pred = labels[y_pred]

    expected_report = """\
              precision    recall  f1-score   support

       blue\xa2       0.83      0.79      0.81        24
      green\xa2       0.33      0.10      0.15        31
        red\xa2       0.42      0.90      0.57        20

    accuracy                           0.53        75
   macro avg       0.53      0.60      0.51        75
weighted avg       0.51      0.53      0.47        75
"""
    report = classification_report(y_true, y_pred)
    assert_equal(report, expected_report) 

def eval(model, test_data, test_label, thresholds=0.5, num_classes=2, pr_figure_path=None, pred_save_path=None):
    print('{0}, val mean acc:{1}'.format(model.__str__(), model.score(test_data, test_label)))
    if num_classes == 2:
        # binary classification
        label_pred_probas = model.predict_proba(test_data)[:, 1]
        label_pred = label_pred_probas > thresholds
        precision, recall, threshold = precision_recall_curve(test_label, label_pred)
        plot_pr(thresholds, precision, recall, figure_path=pr_figure_path)
    else:
        # multi
        label_pred = model.predict(test_data)
        # precision_recall_curve: multiclass format is not supported
    print(classification_report(test_label, label_pred))
    if pred_save_path:
        with open(pred_save_path, 'w', encoding='utf-8') as f:
            for i in label_pred:
                f.write(str(i) + '\n')
    return label_pred 

def simple_evaluate(y_true, y_pred):
    """
    evaluate precision, recall, f1
    :param y_true:
    :param y_pred:
    :return:score
    """
    assert len(y_true) == len(y_pred), \
        "the count of pred label should be same with true label"

    classify_report = metrics.classification_report(y_true, y_pred)
    confusion_matrix = metrics.confusion_matrix(y_true, y_pred)
    overall_accuracy = metrics.accuracy_score(y_true, y_pred)
    acc_for_each_class = metrics.precision_score(y_true, y_pred, average=None)
    average_accuracy = np.mean(acc_for_each_class)
    score = metrics.accuracy_score(y_true, y_pred)
    print('classify_report : \n', classify_report)
    print('confusion_matrix : \n', confusion_matrix)
    print('acc_for_each_class : \n', acc_for_each_class)
    print('average_accuracy: {0:f}'.format(average_accuracy))
    print('overall_accuracy: {0:f}'.format(overall_accuracy))
    print('score: {0:f}'.format(score))
    return score 

def test_classification_report_multiclass_with_digits():
    # Test performance report with added digits in floating point values
    iris = datasets.load_iris()
    y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

    # print classification report with class names
    expected_report = """\
              precision    recall  f1-score   support

      setosa    0.82609   0.79167   0.80851        24
  versicolor    0.33333   0.09677   0.15000        31
   virginica    0.41860   0.90000   0.57143        20

    accuracy                        0.53333        75
   macro avg    0.52601   0.59615   0.50998        75
weighted avg    0.51375   0.53333   0.47310        75
"""
    report = classification_report(
        y_true, y_pred, labels=np.arange(len(iris.target_names)),
        target_names=iris.target_names, digits=5)
    assert_equal(report, expected_report) 

def evaluate(config, model, data_iter, test=False):
    model.eval()
    loss_total = 0
    predict_all = np.array([], dtype=int)
    labels_all = np.array([], dtype=int)
    with torch.no_grad():
        for texts, labels in data_iter:
            outputs = model(texts)
            loss = F.cross_entropy(outputs, labels)
            loss_total += loss
            labels = labels.data.cpu().numpy()
            predic = torch.max(outputs.data, 1)[1].cpu().numpy()
            labels_all = np.append(labels_all, labels)
            predict_all = np.append(predict_all, predic)

    acc = metrics.accuracy_score(labels_all, predict_all)
    if test:
        report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
        confusion = metrics.confusion_matrix(labels_all, predict_all)
        return acc, loss_total / len(data_iter), report, confusion
    return acc, loss_total / len(data_iter) 

def test_classification_report_multiclass_with_label_detection():
    iris = datasets.load_iris()
    y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

    # print classification report with label detection
    expected_report = """\
              precision    recall  f1-score   support

           0       0.83      0.79      0.81        24
           1       0.33      0.10      0.15        31
           2       0.42      0.90      0.57        20

    accuracy                           0.53        75
   macro avg       0.53      0.60      0.51        75
weighted avg       0.51      0.53      0.47        75
"""
    report = classification_report(y_true, y_pred)
    assert_equal(report, expected_report) 

def test_classification_report_multiclass_balanced():
    y_true, y_pred = [0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]

    expected_report = """\
              precision    recall  f1-score   support

           0       0.33      0.33      0.33         3
           1       0.33      0.33      0.33         3
           2       0.33      0.33      0.33         3

    accuracy                           0.33         9
   macro avg       0.33      0.33      0.33         9
weighted avg       0.33      0.33      0.33         9
"""
    report = classification_report(y_true, y_pred)
    assert_equal(report, expected_report) 

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) 

def test_classification_report_multiclass():
    # Test performance report
    iris = datasets.load_iris()
    y_true, y_pred, _ = make_prediction(dataset=iris, binary=False)

    # print classification report with class names
    expected_report = """\
              precision    recall  f1-score   support

      setosa       0.83      0.79      0.81        24
  versicolor       0.33      0.10      0.15        31
   virginica       0.42      0.90      0.57        20

    accuracy                           0.53        75
   macro avg       0.53      0.60      0.51        75
weighted avg       0.51      0.53      0.47        75
"""
    report = classification_report(
        y_true, y_pred, labels=np.arange(len(iris.target_names)),
        target_names=iris.target_names)
    assert_equal(report, expected_report) 

def main():
    args = parse_args()

    features_extractor = FaceFeaturesExtractor()
    embeddings, labels, class_to_idx = load_data(args, features_extractor)
    clf = train(args, embeddings, labels)

    idx_to_class = {v: k for k, v in class_to_idx.items()}

    target_names = map(lambda i: i[1], sorted(idx_to_class.items(), key=lambda i: i[0]))
    print(metrics.classification_report(labels, clf.predict(embeddings), target_names=list(target_names)))

    if not os.path.isdir(MODEL_DIR_PATH):
        os.mkdir(MODEL_DIR_PATH)
    model_path = os.path.join('model', 'face_recogniser.pkl')
    joblib.dump(FaceRecogniser(features_extractor, clf, idx_to_class), model_path) 

def learn_structure(self, samples):
        X_train, X_train_label, X_test, X_test_label = \
            self._generate_train_test_sets(samples, 0.75)
        logger.info('Training with ' + str(len(X_train)) +
                    'samples; testing with ' + str(len(X_test)) + ' samples.')

        rf_detector = self._get_best_detector(X_train, X_train_label)
        Y_test = rf_detector.predict(X_test)

        num_anomalies = Y_test[Y_test == ANOMALY].size
        logger.info('Found ' + str(num_anomalies) +
                    ' anomalies in testing set')

        logger.info('Confusion Matrix: \n{}'.
                    format(classification_report(
                        X_test_label,
                        Y_test,
                        target_names=['no', 'yes'])))
        return rf_detector 

def learn_structure(self, samples):
        X_train, X_train_label, X_test, X_test_label = \
            self._generate_train_test_sets(samples, 0.75)
        logger.info('Training with ' + str(len(X_train)) +
                    'samples; testing with ' + str(len(X_test)) + ' samples.')

        lr_detector = self._get_best_detector(X_train, X_train_label)
        Y_test = lr_detector.predict(X_test)

        num_anomalies = Y_test[Y_test == ANOMALY].size
        logger.info('Found ' + str(num_anomalies) +
                    ' anomalies in testing set')

        logger.info('Confusion Matrix: \n{}'.
                    format(classification_report(
                        X_test_label,
                        Y_test,
                        target_names=['no', 'yes'])))
        return lr_detector 

def learn_structure(self, samples):
        X_train, X_train_label, X_test, X_test_label = \
            self._generate_train_test_sets(samples, 0.75)
        logger.info('Training with ' + str(len(X_train)) +
                    'samples; testing with ' + str(len(X_test)) + ' samples.')

        dt_detector = self._get_best_detector(X_train, X_train_label)
        Y_test = dt_detector.predict(X_test)

        num_anomalies = Y_test[Y_test == ANOMALY].size
        logger.info('Found ' + str(num_anomalies) +
                    ' anomalies in testing set')

        logger.info('Confusion Matrix: \n{}'.
                    format(classification_report(
                        X_test_label,
                        Y_test,
                        target_names=['no', 'yes'])))
        return dt_detector 

def test_classification_report_multiclass_with_long_string_label():
    y_true, y_pred, _ = make_prediction(binary=False)

    labels = np.array(["blue", "green" * 5, "red"])
    y_true = labels[y_true]
    y_pred = labels[y_pred]

    expected_report = """\
                           precision    recall  f1-score   support

                     blue       0.83      0.79      0.81        24
greengreengreengreengreen       0.33      0.10      0.15        31
                      red       0.42      0.90      0.57        20

                 accuracy                           0.53        75
                macro avg       0.53      0.60      0.51        75
             weighted avg       0.51      0.53      0.47        75
"""

    report = classification_report(y_true, y_pred)
    assert_equal(report, expected_report) 

def compute_score(predictions, ground, dataset, emotion2id, test=False):
        pred_y = predictions.astype(int)
        val_y = ground.astype(int)
        
        if dataset in ["EC"]:
            labels = [emotion2id["happy"], emotion2id["angry"], emotion2id["sad"]]
            score = f1_score(val_y, pred_y, average='micro', labels=labels)
            print("Micro-F1 (exclude neutral): {0}".format(score))
            if test:
                print(classification_report(val_y, pred_y, labels=labels, digits=4))
        elif dataset in ["DD"]:
            labels = [emotion2id[str(i)] for i in range(1, 7)]
            score = f1_score(val_y, pred_y, average='micro', labels=labels)
            print("Micro-F1 (exclude neutral): {0}".format(score))
            if test:
                print(classification_report(val_y, pred_y, labels=labels, digits=4))
        elif dataset in ["MELD", "IEMOCAP", "EmoryNLP"]:
            score = f1_score(val_y, pred_y, average='weighted')
            print("Weighted Macro-F1: {0}".format(score))
            if test:
                print(classification_report(val_y, pred_y, digits=4))
        else:
            score = mean_absolute_error(val_y, pred_y)
            print("MAE: {0}".format(score))
            if test:
                print(mean_absolute_error(val_y, pred_y))
        return score 

def _score(self, vectorizer, clf, x_train, y_train, x_test, y_test, output):
        text_clf = Pipeline([
            ('vectorizer', vectorizer),
            ('clf', clf)])
        text_clf.fit(x_train, y_train)
        predicted = text_clf.predict(x_test)
        report = metrics.classification_report(y_test, predicted)
        if output == 'Stdout':
            for index, (input, prediction, label) in enumerate(zip (x_test, predicted, y_test)):
                if prediction != label:
                    print('Text:', input, 'has been classified as:', prediction, 'should be:', label)
            print(report)
        else:
            write_file(os.path.join(os.getcwd(), 'report'), report) 

def runClassReport(clf, A, Cl):
    from sklearn.metrics import classification_report
    y_pred = clf.predict(A)
    print(classification_report(Cl, y_pred, target_names=clf.classes_))
    print(' Precision is the probability that, given a classification result for a sample,\n' +
          ' the sample actually belongs to that class. Recall (Accuracy) is the probability that a \n' +
          ' sample will be correctly classified for a given class. f1-score combines both \n' +
          ' accuracy and precision to give a single measure of relevancy of the classifier results.\n')

#************************************