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| import numpy as np
import operator
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, precision_recall_fscore_support, roc_curve, auc, precision_recall_curve, average_precision_score
from sklearn.preprocessing import label_binarize
def classify0(in_x, data_set, labels, k):
"""KNN 分类器：返回预测标签与邻居投票统计。"""
data_set_size = data_set.shape[0]
diff_mat = np.tile(in_x, (data_set_size, 1)) - data_set
distances = (diff_mat**2).sum(axis=1) ** 0.5
sorted_idx = distances.argsort()
votes = {}
for i in range(k):
lab = labels[sorted_idx[i]]
votes[lab] = votes.get(lab, 0) + 1
pred = sorted(votes.items(), key=operator.itemgetter(1), reverse=True)[0][0]
return pred, votes
def file2matrix(filename):
"""从文件读取数据并转为矩阵与标签。"""
with open(filename) as fr:
lines = fr.readlines()
m = len(lines)
data = np.zeros((m, 3))
labels = []
for i, line in enumerate(lines):
parts = line.strip().split('\t')
data[i, :] = parts[0:3]
labels.append(parts[-1])
return data, labels
def auto_norm(data_set):
"""按列做 min-max 归一化。"""
min_vals = data_set.min(0)
max_vals = data_set.max(0)
ranges = max_vals - min_vals
m = data_set.shape[0]
norm = (data_set - np.tile(min_vals, (m, 1))) / np.tile(ranges, (m, 1))
return norm, ranges, min_vals
def plot_confusion_matrix(y_true, y_pred, classes, title='Confusion matrix', cmap=plt.cm.Blues):
cm = confusion_matrix(y_true, y_pred)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
ticks = np.arange(len(classes))
plt.xticks(ticks, classes, rotation=45)
plt.yticks(ticks, classes)
fmt = 'd'
thresh = cm.max() / 2.0
for i, j in np.ndindex(cm.shape):
plt.text(j, i, format(cm[i, j], fmt),
ha="center", color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
plt.savefig('confusion_matrix.png')
plt.close()
def plot_roc_curve(y_true, y_scores, classes):
y_true_bin = label_binarize(y_true, classes=np.unique(y_true))
n_classes = y_true_bin.shape[1]
fpr, tpr, roc_auc = {}, {}, {}
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve(y_true_bin[:, i], y_scores[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
plt.figure()
colors = ['aqua', 'darkorange', 'cornflowerblue']
for i, color in zip(range(n_classes), colors):
plt.plot(fpr[i], tpr[i], color=color, lw=2,
label=f'ROC curve of class {i+1} (area = {roc_auc[i]:0.2f})')
plt.plot([0, 1], [0, 1], 'k--', lw=2)
plt.xlim([0.0, 1.0]); plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate'); plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc="lower right")
plt.savefig('roc_curve.png')
plt.close()
def plot_pr_curve(y_true, y_scores, classes):
y_true_bin = label_binarize(y_true, classes=np.unique(y_true))
n_classes = y_true_bin.shape[1]
precision, recall, ap = {}, {}, {}
for i in range(n_classes):
precision[i], recall[i], _ = precision_recall_curve(y_true_bin[:, i], y_scores[:, i])
ap[i] = average_precision_score(y_true_bin[:, i], y_scores[:, i])
plt.figure()
colors = ['aqua', 'darkorange', 'cornflowerblue']
for i, color in zip(range(n_classes), colors):
plt.plot(recall[i], precision[i], color=color, lw=2,
label=f'PR curve of class {i+1} (AP = {ap[i]:0.2f})')
plt.xlabel('Recall'); plt.ylabel('Precision'); plt.title('Precision-Recall Curve')
plt.legend(loc="lower left")
plt.savefig('pr_curve.png')
plt.close()
def dating_class_test():
"""主流程：加载数据 -> 归一化 -> 拆分 -> 预测 -> 评估与可视化。"""
ho_ratio = 0.10
data_raw, label_str = file2matrix('datingTestSet.txt')
label_map = {'didntLike': 1, 'smallDoses': 2, 'largeDoses': 3}
labels = [label_map[x] for x in label_str]
data_norm, ranges, min_vals = auto_norm(data_raw)
m = data_norm.shape[0]
n_test = int(m * ho_ratio)
train_x = data_norm[n_test:, :]
train_y = labels[n_test:]
test_x = data_norm[:n_test, :]
test_y = labels[:n_test]
k = 3
preds, scores = [], []
for i in range(n_test):
pred, vote = classify0(test_x[i, :], train_x, train_y, k)
preds.append(pred)
s = np.zeros(len(label_map))
for lab, cnt in vote.items():
s[lab - 1] = cnt / k
scores.append(s)
scores = np.array(scores)
err = (np.array(preds) != np.array(test_y)).sum()
acc = 1 - err / float(n_test)
print(f"Accuracy: {acc:.2f}")
precision, recall, f1, _ = precision_recall_fscore_support(test_y, preds, average='macro')
print(f"Precision: {precision:.2f}, Recall: {recall:.2f}, F1-Score: {f1:.2f}")
class_names = list(label_map.keys())
plot_confusion_matrix(test_y, preds, classes=class_names)
plot_roc_curve(test_y, scores, classes=np.unique(labels))
plot_pr_curve(test_y, scores, classes=np.unique(labels))
print("评估图表 (confusion_matrix.png, roc_curve.png, pr_curve.png) 已保存。")
if __name__ == '__main__':
dating_class_test()
|
