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import numpy as np
from sklearn.metrics import *
from sklearn.preprocessing import label_binarize
from visualization.draw_line_graph import draw_line_graph
class ClassificationMetrics:
@classmethod
def get_metrics(cls):
return ["Accuracy", "Precision", "Recall", "F1-score"]
def calculate_classification_metrics(pred_data, real_data):
info = {}
real_data = np.round(real_data, 0).astype(int)
pred_data = np.round(pred_data, 0).astype(int)
cur_confusion_matrix = confusion_matrix(real_data[:, 0], pred_data)
info["Confusion matrix"] = cur_confusion_matrix
info["Accuracy"] = np.sum(cur_confusion_matrix.diagonal()) / np.sum(cur_confusion_matrix)
info["Precision"] = cur_confusion_matrix.diagonal() / np.sum(cur_confusion_matrix, axis=1)
info["Recall"] = cur_confusion_matrix.diagonal() / np.sum(cur_confusion_matrix, axis=0)
info["F1-score"] = np.mean(2 * np.multiply(info["Precision"], info["Recall"]) / (info["Precision"] + info["Recall"]))
return info
max_class = max(real_data)[0]
min_class = min(real_data)[0]
pred_data_ = label_binarize(pred_data, classes=range(min_class, max_class+1))
real_data_ = label_binarize(real_data, classes=range(min_class, max_class+1))
for i in range(max_class - min_class):
fpr, tpr, thresholds = roc_curve(real_data_[:, i], pred_data_[:, i])
# draw_line_graph(fpr, tpr, "ROC curve with AUC={:.2f}".format(auc(fpr, tpr)))
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