The supervised learning framework based on perceptron for tabular data.
Project description
perming
perming: Perceptron Models Are Training on Windows Platform with Default GPU Acceleration.
- p: use polars or pandas to read dataset.
- per: perceptron algorithm used as based model.
- m: models concluding regressier and classifier (binary & multiple).
- ing: training on windows platform with strong gpu acceleration.
init backend
refer to https://pytorch.org/get-started/locally/ and choose the PyTorch that support cuda compatible with your Windows. The current software version only supports Windows system.
test with: PyTorch 1.7.1+cu101
general model
| GENERAL_BOX(Box) | Parameters | Meaning |
|---|---|---|
__init__ |
input_: int num_classes: int hidden_layer_sizes: Tuple[int]=(100,) device: str="cuda" * activation: str="relu" inplace_on: bool=True criterion: str="CrossEntropyLoss" solver: str="adam" batch_size: int=32 learning_rate_init: float=1e-2 lr_scheduler: Optional[str]=None |
Initialize Classifier or Regressier Based on Basic Information of the Dataset Obtained through Data Preprocessing and Feature Engineering. |
| print_config | / | Return Initialized Parameters of Multi-layer Perceptron and Graph. |
| data_loader | features: TabularData labels: TabularData ratio_set: Dict[str, int]={'train': 8, 'test': 1, 'val': 1} worker_set: Dict[str, int]={'train': 8, 'test': 2, 'val': 1} random_seed: Optional[int]=None |
Using ratio_set and worker_set to Load the Numpy Dataset into torch.utils.data.DataLoader. |
| train_val | num_epochs: int=2 tolerance: float=1e-3 patience: int=10 interval: int=100 backend: str="threading" n_jobs: int=-1 early_stop: bool=False |
Using num_epochs, tolerance, patience to Control Training Process and interval to Adjust Print Interval with Accelerated Validation Combined with backend and n_jobs. |
| test | sort_by: str="accuracy" sort_state: bool=True |
Sort Returned Test Result about Correct Classes with sort_by and sort_state Which Only Appears in Classification. |
| save | show: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by show. |
| load | show: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by show. |
common models (cuda first)
- Regression
| Regressier | Parameters | Meaning |
|---|---|---|
__init__ |
input_: int hidden_layer_sizes: Tuple[int]=(100,) * activation: str="relu" criterion: str="MSELoss" solver: str="adam" batch_size: int=32 learning_rate_init: float=1e-2 lr_scheduler: Optional[str]=None |
Initialize Regressier Based on Basic Information of the Regression Dataset Obtained through Data Preprocessing and Feature Engineering with num_classes=1. |
| print_config | / | Return Initialized Parameters of Multi-layer Perceptron and Graph. |
| data_loader | features: TabularData labels: TabularData ratio_set: Dict[str, int]={'train': 8, 'test': 1, 'val': 1} worker_set: Dict[str, int]={'train': 8, 'test': 2, 'val': 1} random_seed: Optional[int]=None |
Using ratio_set and worker_set to Load the Regression Dataset with Numpy format into torch.utils.data.DataLoader. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str="threading" n_jobs: int=-1 early_stop: bool=False |
Using num_epochs, tolerance, patience to Control Training Process and interval to Adjust Print Interval with Accelerated Validation Combined with backend and n_jobs. |
| test | / | Test Module Only Show with Loss at 3 Stages: Train, Test, Val |
| save | show: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by show. |
| load | show: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by show. |
- Binary-classification
| Binarier | Parameters | Meaning |
|---|---|---|
__init__ |
input_: int hidden_layer_sizes: Tuple[int]=(100,) * activation: str="relu" criterion: str="BCELoss" solver: str="adam" batch_size: int=32 learning_rate_init: float=1e-2 lr_scheduler: Optional[str]=None |
Initialize Classifier Based on Basic Information of the Classification Dataset Obtained through Data Preprocessing and Feature Engineering with num_classes=2. |
| print_config | / | Return Initialized Parameters of Multi-layer Perceptron and Graph. |
| data_loader | features: TabularData labels: TabularData ratio_set: Dict[str, int]={'train': 8, 'test': 1, 'val': 1} worker_set: Dict[str, int]={'train': 8, 'test': 2, 'val': 1} random_seed: Optional[int]=None |
Using ratio_set and worker_set to Load the Binary-classification Dataset with Numpy format into torch.utils.data.DataLoader. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str="threading" n_jobs: int=-1 early_stop: bool=False |
Using num_epochs, tolerance, patience to Control Training Process and interval to Adjust Print Interval with Accelerated Validation Combined with backend and n_jobs. |
| test | sort_by: str="accuracy" sort_state: bool=True |
Test Module Show with Correct Class and Loss at 3 Stages: Train, Test, Val |
| save | show: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by show. |
| load | show: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by show. |
- Multi-classification
| Multipler | Parameters | Meaning |
|---|---|---|
__init__ |
input_: int num_classes: int hidden_layer_sizes: Tuple[int]=(100,) * activation: str="relu" criterion: str="CrossEntropyLoss" solver: str="adam" batch_size: int=32 learning_rate_init: float=1e-2 lr_scheduler: Optional[str]=None |
Initialize Classifier Based on Basic Information of the Classification Dataset Obtained through Data Preprocessing and Feature Engineering with num_classes>2. |
| print_config | / | Return Initialized Parameters of Multi-layer Perceptron and Graph. |
| data_loader | features: TabularData labels: TabularData ratio_set: Dict[str, int]={'train': 8, 'test': 1, 'val': 1} worker_set: Dict[str, int]={'train': 8, 'test': 2, 'val': 1} random_seed: Optional[int]=None |
Using ratio_set and worker_set to Load the Multi-classification Dataset with Numpy format into torch.utils.data.DataLoader. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str="threading" n_jobs: int=-1 early_stop: bool=False |
Using num_epochs, tolerance, patience to Control Training Process and interval to Adjust Print Interval with Accelerated Validation Combined with backend and n_jobs. |
| test | sort_by: str="accuracy" sort_state: bool=True |
Sort Returned Test Result about Correct Classes with sort_by and sort_state Which Only Appears in Classification. |
| save | show: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by show. |
| load | show: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by show. |
- Multi-outputs
| Ranker | Parameters | Meaning |
|---|---|---|
__init__ |
input_: int num_outputs: int hidden_layer_sizes: Tuple[int]=(100,) * activation: str="relu" criterion: str="MultiLabelSoftMarginLoss" solver: str="adam" batch_size: int=32 learning_rate_init: float=1e-2 lr_scheduler: Optional[str]=None |
Initialize Ranker Based on Basic Information of the Classification Dataset Obtained through Data Preprocessing and Feature Engineering with (n_samples, n_outputs). |
| print_config | / | Return Initialized Parameters of Multi-layer Perceptron and Graph. |
| data_loader | features: TabularData labels: TabularData ratio_set: Dict[str, int]={'train': 8, 'test': 1, 'val': 1} worker_set: Dict[str, int]={'train': 8, 'test': 2, 'val': 1} random_seed: Optional[int]=None |
Using ratio_set and worker_set to Load the Multi-outputs Dataset with Numpy format into torch.utils.data.DataLoader. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str="threading" n_jobs: int=-1 early_stop: bool=False |
Using num_epochs, tolerance, patience to Control Training Process and interval to Adjust Print Interval with Accelerated Validation Combined with backend and n_jobs. |
| test | sort_by: str="accuracy" sort_state: bool=True |
Sort Returned Test Result about Correct Classes with sort_by and sort_state Which Only Appears in Classification. |
| save | show: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by show. |
| load | show: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by show. |
prefer replace target shape (n,1) with shape (n,) using numpy.squeeze(input_matrix)
pip install
download latest version:
git clone https://github.com/linjing-lab/easy-pytorch.git
cd easy-pytorch/released_box
pip install -e . --verbose
download stable version:
pip install perming --upgrade
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