"sparsemax" 更加常用。 mask_type 参数用于指定特征选择掩码的类型,控制模型在每个决策步骤中选择特征的方式。 3. 其他参数 seed 类型: int 默认值: 0 描述: 随机种子,用于确保结果的可重复性。 verbose 类型: int 默认值: 1 描述: 控制输出日志的详细程度。0 表示静默模式,1 表示普通模式,2 表示调试模式。
Tsallis losses are a family of losses parametrized by a positive real value α. They recover the multinomial logistic loss with α=1 and the sparsemax loss with α=2. Values of α between 1 and 2 enable to interpolate between the two losses. ...
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant invest
sparsemax.py 1 Apr 13, 2024 t.py 1 Apr 23, 2024 Repository files navigation README (Time Series Domain Adaptation via Sparse Associative Structure Alignment)[AAAI2021]RequirementsPython 3.7 Pytorch 1.7 Quick StartThe pytorch version of sasa basically replicated the results of the tensorflow version...
python experiments/signal-game/train.py \ --mode marg \ --normalizer sparsemax \ --lr 0.005 \ --entropy_coeff 0.1 \ --batch_size 64 \ --n_epochs 500 \ --game_size 16 \ --latent_size 256 \ --embedding_size 256 \ --hidden_size 512 \ --weight_decay 0. \ --random_seed 42 ...
StepLR, mask_type='sparsemax' # This will be overwritten if using pretrain model ) clf.fit( X_train=X_train, y_train=y_train, eval_set=[(X_train, y_train), (X_valid, y_valid)], eval_name=['train', 'valid'], eval_metric=['auc'], from_unsupervised=unsupervised_model ) The...
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant invest
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant invest
# TabNetPretrainerunsupervised_model=TabNetPretrainer(optimizer_fn=torch.optim.Adam,optimizer_params=dict(lr=2e-2),mask_type='entmax'# "sparsemax")unsupervised_model.fit(X_train=X_train,eval_set=[X_valid],pretraining_ratio=0.8, )clf=TabNetClassifier(optimizer_fn=torch.optim.Adam,optimizer_params...