计算多标签评价指标subset accuracy的python代码

下面是计算多标签评价指标subset accuracy的Python代码：

def subset_accuracy(y_true, y_pred):
    """
    计算多标签评价指标subset accuracy
    :param y_true: 真实标签，二维数组，每个样本可能有多个标签，标签用0或1表示
    :param y_pred: 预测标签，二维数组，每个样本可能有多个标签，标签用0或1表示
    :return: subset accuracy
    """
    num_samples = len(y_true)
    correct = 0
    for i in range(num_samples):
        if np.array_equal(y_true[i], y_pred[i]):
            correct += 1
    return float(correct) / num_samples

其中，`y_true`和`y_pred`都是二维数组，每个样本可能有多个标签，标签用0或1表示。函数首先计算样本数`num_samples`，然后遍历每个样本，如果它们的真实标签和预测标签完全一致，那么就认为它们被分类正确，将`correct`计数器加1。最后，将正确分类的样本数除以总样本数，得到subset accuracy。

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眼科疾病分类python代码

以下是一个简单的眼科疾病分类的Python代码示例，使用了Keras深度学习框架和眼科疾病图像数据集：

import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 设置GPU内存增长
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

# 数据路径和参数
data_path = 'path/to/dataset/'
img_height = 256
img_width = 256
batch_size = 32

# 数据预处理
train_ds = keras.preprocessing.image_dataset_from_directory(
    data_path,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

val_ds = keras.preprocessing.image_dataset_from_directory(
    data_path,
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

class_names = train_ds.class_names
num_classes = len(class_names)

# 数据增强
data_augmentation = keras.Sequential(
  [
    layers.experimental.preprocessing.RandomFlip("horizontal", 
                                                 input_shape=(img_height, 
                                                              img_width,
                                                              3)),
    layers.experimental.preprocessing.RandomRotation(0.1),
    layers.experimental.preprocessing.RandomZoom(0.1),
  ]
)

# 构建模型
model = keras.Sequential([
    data_augmentation,
    layers.experimental.preprocessing.Rescaling(1./255),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(128, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(num_classes)
])

# 编译模型
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 训练模型
epochs = 10
model.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)

# 评估模型
test_ds = keras.preprocessing.image_dataset_from_directory(
    data_path,
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

loss, accuracy = model.evaluate(test_ds)
print("Accuracy", accuracy)

这个示例代码使用了卷积神经网络（CNN）来训练眼科疾病分类模型。数据集使用了眼科图像数据集，预处理包括数据增强和归一化。在训练过程中，使用了验证集进行模型性能评估，并在测试集上进行最终评估。

easyensemble算法的实现python代码

以下是 EasyEnsemble 算法的一个简单实现的 Python 代码示例：

from sklearn.tree import DecisionTreeClassifier
from sklearn.base import clone
from sklearn.utils import resample
import numpy as np

class EasyEnsemble:
    def __init__(self, base_classifier=DecisionTreeClassifier(), n_estimators=10):
        self.base_classifier = base_classifier
        self.n_estimators = n_estimators
        self.ensemble = []

    def fit(self, X, y):
        for _ in range(self.n_estimators):
            # 通过自助采样创建一个子集
            X_subset, y_subset = resample(X, y)
            # 训练基分类器
            classifier = clone(self.base_classifier)
            classifier.fit(X_subset, y_subset)
            # 将基分类器添加到集合中
            self.ensemble.append(classifier)

    def predict(self, X):
        # 使用投票机制进行预测
        predictions = np.zeros((X.shape[0], self.n_estimators))
        for i, classifier in enumerate(self.ensemble):
            predictions[:, i] = classifier.predict(X)
        return np.round(np.mean(predictions, axis=1))

使用示例：

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 创建一个示例数据集
X, y = make_classification(n_samples=1000, n_features=10, random_state=42)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建 EasyEnsemble 分类器并进行训练
ensemble = EasyEnsemble(n_estimators=10)
ensemble.fit(X_train, y_train)

# 进行预测并评估准确性
y_pred = ensemble.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

请注意，这只是一个简单的实现示例，EasyEnsemble 算法的完整实现可能涉及更多的参数选择、特征选择和模型优化技术。此外，你可以根据自己的需求进行修改和改进。