perming 1.4.1

The supervised learning framework based on perceptron for tabular data.

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 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