ABLkit/abl/models

• basic_model.py
• • BasicModel 类提供的接口
  • BasicModel 类的参数
  • 例子
• wabl_models.py
• • WABLBasicModel 类提供的接口
  • WABLBasicModel 类的参数
  • 序列化数据
  • 例子

basic_model.py

可以使用basic_model.py中实现的BasicModel类将pytorch神经网络模型包装成sklearn模型的形式.

BasicModel 类提供的接口

方法	功能
fit(X, y)	训练神经网络
predict(X)	预测 X 的类别
predict_proba(X)	预测 X 的类别概率
score(X, y)	计算模型在测试数据上的准确率
save()	保存模型
load()	加载模型

BasicModel 类的参数

model : torch.nn.Module

• The PyTorch model to be trained or used for prediction.

batch_size : int

• The batch size used for training.

num_epochs : int

• The number of epochs used for training.

stop_loss : Optional[float]

• The loss value at which to stop training.

num_workers : int

• The number of workers used for loading data.

criterion : torch.nn.Module

• The loss function used for training.

optimizer : torch.nn.Module

• The optimizer used for training.

transform : Callable[..., Any]

• The transformation function used for data augmentation.

device : torch.device

• The device on which the model will be trained or used for prediction.

recorder : Any

• The recorder used to record training progress.

save_interval : Optional[int]

• The interval at which to save the model during training.

save_dir : Optional[str]

• The directory in which to save the model during training.

collate_fn : CallableList[T, Any]

• The function used to collate data.

例子

# Three necessary component
cls = LeNet5()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cls.parameters())

# Initialize base_model
base_model = BasicModel(
    cls,
    criterion,
    optimizer,
    torch.device("cuda:0"),
    batch_size=32,
    num_epochs=10,
)

# Prepare data
train_X, train_y = get_train_data()
test_X, test_y = get_test_data()

# Train model
base_model.fit(train_X, train_y)

# Predict
base_model.predict(test_X)

# Validation
base_model.score(test_X, test_y)

wabl_models.py

wabl_models.py中实现的WABLBasicModel能够序列化数据并为不同的机器学习模型提供统一的接口.

WABLBasicModel 类提供的接口

方法	功能
train(X, Y)	利用训练数据训练机器学习模型（不涉及反绎）
predict(X)	预测 X 的类别和概率
valid(X, Y)	计算模型在测试数据上的准确率

WABLBasicModel 类的参数

base_model : Machine Learning Model

• The base model to use for training and prediction.

pseudo_label_list : List[Any]

• A list of pseudo labels to use for training.

序列化数据

考虑到训练数据可能多种组织形式，比如：
X: List[List[img]], Y: List[List[label]]
X: List[List[img]], Y: List[label]
X: List[img], Y: List[label]
...
不便于训练. 因此先将形式统一为：X: List[img], Y: List[label]，也就是所谓的序列化数据.

例子

# Three necessary component
# 'ml_model' is no longer limited to NN models
model = WABLBasicModel(ml_model, kb.pseudo_label_list)

# Prepare data
train_X, train_y = get_train_data()
test_X, test_y = get_test_data()

# Train model
model.train(train_X, train_y)

# Predict
model.predict(test_X)

# Validation
model.valid(test_X, test_y)