whujaketest 1.0.10

A small example package

python setup.py sdist upload

python setup.py sdist bdist_wheel

C:\Users\zx\AppData\Roaming\Python\Python38\Scripts\twine.exe

C:\Users\zx\AppData\Roaming\Python\Python38\Scripts\twine.exe upload --repository-url https://upload.pypi.org/legacy/ dist/*

['Has0_acc_2', 'Has0_F1_score', 'Non0_acc_2', 'Non0_F1_score', 'Mult_acc_5', 'Mult_acc_7', 'MAE', 'Corr', 'LOSS']

ted. Migrate to API Tokens or Trusted Publishers instead. See https://pypi.org/help/#apitoken and https://pypi.org/help/#trusted-publishers

pypi-AgEIcHlwaS5vcmcCJDM4ZWVlYWZjLWUyNTItNDFiOS04ZThhLWY3ZTgxMjY1MGE5OAACKlszLCJiYWQyNDk0NS01YzkzLTQyMGQtYWJjNS1mZTUyYTc1ZDNjODIiXQAABiB0Pi624udpN2f7xbfGJKXKpWvtQ-pJRSs4M-YeayUJAQ

pypi-AgEIcHlwaS5vcmcCJDJjZTU4NzBiLTIwYWEtNGZlMi1iZTU4LWJjMmYzYTQ5MzU2MwACE1sxLFsid2h1amFrZXRlc3QiXV0AAixbMixbImU3MzNlMmYwLTRhNGQtNGYxMi1hNzRmLTUwMDIxNmMyNjA2MSJdXQAABiBCuwC0q4PHXYNTX8lS6OCmneQhIj_xJIqWbfZFuY0eew

pip install opencv-python

python setup.py sdist bdist_wheel

import tarfile

def extract_tar_gz(file_path, extract_path): with tarfile.open(file_path, 'r:gz') as tar: tar.extractall(extract_path)

调用示例

file_path = '/path/to/file.tar.gz' extract_path = '/path/to/extract' extract_tar_gz(file_path, extract_path)

torchvision torchaudio opencv_python

pip install torch -i https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/

pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple

pip3 install torch --index-url https://download.pytorch.org/whl/cu118

https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/win-64/

import numpy as np from sklearn.datasets import make_classification_target from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split

生成示例数据

X, y = make_classification_target(n_samples=1000, n_features=50, n_classes=5, n_informative=8, n_redundant=10, random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

创建随机森林分类器实例

random_forest = RandomForestClassifier(n_estimators=100, random_state=42)

训练模型

random_forest.fit(X_train, y_train)

进行预测

y_pred = random_forest.predict(X_test)

计算准确率

accuracy = np.sum(y_pred == y_test) / len(y_test) print(f"Accuracy: {accuracy}")

from sklearn import metrics metrics.precision_score(y_true, y_pred, average='micro') # 微平均，精确率 Out[130]: 0.33333333333333331

metrics.precision_score(y_true, y_pred, average='macro') # 宏平均，精确率 Out[131]: 0.375

metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro') # 指定特定分类标签的精确率 Out[133]: 0.5

metrics.recall_score(y_true, y_pred, average='micro') Out[134]: 0.33333333333333331

metrics.recall_score(y_true, y_pred, average='macro') Out[135]: 0.3125

metrics.f1_score(y_true, y_pred, average='weighted')
Out[136]: 0.37037037037037035

分类报告：precision/recall/fi-score/均值/分类个数

from sklearn.metrics import classification_report y_true = [0, 1, 2, 2, 0] y_pred = [0, 0, 2, 2, 0] target_names = ['class 0', 'class 1', 'class 2'] print(classification_report(y_true, y_pred, target_names=target_names))

import numpy as np import matplotlib.pyplot as plt from itertools import cycle

from sklearn import svm, datasets from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from scipy import interp

Import some data to play with

iris = datasets.load_iris() X = iris.data y = iris.target

画图

all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

Then interpolate all ROC curves at this points

mean_tpr = np.zeros_like(all_fpr) for i in range(n_classes): mean_tpr += interp(all_fpr, fpr[i], tpr[i])

Finally average it and compute AUC

mean_tpr /= n_classes

fpr["macro"] = all_fpr tpr["macro"] = mean_tpr roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

Plot all ROC curves

plt.figure() plt.plot(fpr["micro"], tpr["micro"], label='micro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["micro"]), color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"], label='macro-average ROC curve (area = {0:0.2f})' ''.format(roc_auc["macro"]), color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue']) for i, color in zip(range(n_classes), colors): plt.plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC curve of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Some extension of Receiver operating characteristic to multi-class') plt.legend(loc="lower right") plt.show()

.cache\torch\hub\checkpoints