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 include Box, Regressier, Binarier, Mutipler and Ranker.
- ing: training on windows platform with strong gpu acceleration.
init backend
refer to https://pytorch.org/get-started/locally/ and choose PyTorch to support cuda compatible with your Windows.
tests with: PyTorch 1.7.1+cu101
advices
- If users don't want to encounter CUDA out of memory return from joblib.parallel, the best solution is to download v1.9.2 or before v1.6.1.
- If users have no plan to retrain a full network in tuning model, the best solution is to download versions after v1.8.0 which support set_freeze.
- If users are not conducting experiments on Jupyter, download versions after v1.7.* will accelerate train_val process and reduce redundancy.
parameters
init:
- input_: int, feature dimensions of tabular datasets after extract, transform, load from any data sources.
- num_classes: int, define numbers of classes or outputs after users defined the type of task with layer output.
- hidden_layer_sizes: Tuple[int]=(100,), define numbers and sizes of hidden layers to enhance model representation.
- device: str='cuda', configure training and validation device with torch.device options. 'cuda' or 'cpu'.
- activation: str='relu', configure activation function combined with subsequent learning task. see _activate in open models.
- inplace_on: bool=False, configure whether to enable inplace=True on activation. False or True. (manually set in Box)
- criterion: str='CrossEntropyLoss', configure loss criterion with compatible learning task output. see _criterion in open models.
- solver: str='adam', configure inner optimizer serve as learning solver for learning task. see _solver in _utils/BaseModel.
- batch_size: int=32, define batch size on loaded dataset of one epoch training process. any int value > 0. (prefer 2^n)
- learning_rate_init: float=1e-2, define initial learning rate of solver input param controled by inner assertion. (1e-6, 1.0).
- lr_scheduler: Optional[str]=None, configure scheduler about learning rate decay for compatible use. see _scheduler in _utils/BaseModel.
data_loader:
- features: TabularData, manually input by users.
- target: TabularData, manually input by users.
- ratio_set: Dict[str, int]={'train': 8, 'test': 1, 'val': 1}, define by users.
- worker_set: Dict[str, int]={'train': 8, 'test': 2, 'val': 1}, manually set by users need.
- random_seed: Optional[int]=None, manually set any int value by users to fixed sequence.
set_freeze:
- require_grad: Dict[int, bool], manually set freezed layers by given serial numbers according to
self.model. (if users set require_grad with{0: False}, it means freeze the first layer ofself.model.)
train_val:
- num_epochs: int=2, define numbers of epochs in main training cycle. any int value > 0.
- interval: int=100, define console print length of whole epochs by interval. any int value > 0.
- tolerance: float=1e-3, define tolerance used to set inner break sensitivity. (1e-9, 1.0).
- patience: int=10, define value coordinate with tolerance to expand detect length. [10, 100].
- backend: str='threading', configure accelerate backend used in inner process. 'threading', 'multiprocessing', 'loky'.
- n_jobs: int=-1, define numbers of jobs with manually set by users need. -1 or any int value > 0. (if n_jobs=1, parallel processing will be turn off to save cuda memory.)
- prefer: str='threads', configure soft hint to choose the default backend. 'threads', 'processes'. (prefer 'threading' & 'threads' when users try fails by setting 'loky' and 'processes' or turn to v1.6.1.)
- early_stop: bool=False, define whether to enable early_stop process. False or True.
test:
- sort_by: str='accuracy', configure sorted ways of correct_class. 'numbers', 'accuracy', 'num-total'.
- sort_state: bool=True, configure sorted state of correct_class. False or True.
save or load:
- con: bool=True, configure whether to print model.state_dict(). False or True.
- dir: dir='./model', configure model path that save to or load from. correct path defined by users.
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=False 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 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str='threading' n_jobs: int=-1 prefer: str='threads' 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 | con: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by con. |
| load | con: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by con. |
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. |
| set_freeze | require_grad: Dict[int, bool] | freeze some layers by given requires_grad=False if trained model will be loaded to execute experiments. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str='threading' n_jobs: int=-1 prefer: str='threads' 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 | con: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by con. |
| load | con: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by con. |
- Binary-classification
| Binarier | Parameters | Meaning |
|---|---|---|
__init__ |
input_: 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 Binary-classification Dataset with Numpy format into torch.utils.data.DataLoader. |
| set_freeze | require_grad: Dict[int, bool] | freeze some layers by given requires_grad=False if trained model will be loaded to execute experiments. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str='threading' n_jobs: int=-1 prefer: str='threads' 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 con with Correct Class and Loss at 3 Stages: Train, Test, Val |
| save | con: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by con. |
| load | con: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by con. |
- Multi-classification
| Mutipler | 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. |
| set_freeze | require_grad: Dict[int, bool] | freeze some layers by given requires_grad=False if trained model will be loaded to execute experiments. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str='threading' n_jobs: int=-1 prefer: str='threads' 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 | con: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by con. |
| load | con: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by con. |
- 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. |
| set_freeze | require_grad: Dict[int, bool] | freeze some layers by given requires_grad=False if trained model will be loaded to execute experiments. |
| train_val | num_epochs: int=2 interval: int=100 tolerance: float=1e-3 patience: int=10 backend: str='threading' n_jobs: int=-1 prefer: str='threads' 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 | con: bool=True dir: str='./model' |
Save Trained Model Parameters with Model state_dict Control by con. |
| load | con: bool=True dir: str='./model' |
Load Trained Model Parameters with Model state_dict Control by con. |
prefer replace target shape (n,1) with shape (n,) using numpy.squeeze(target), users can search and combine more predefined options in submodules and its __doc__ of each open classes.
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
download versions without supported early_stop:
pip install perming==1.3.1
download versions with supported early_stop:
pip install perming>=1.4.1
download versions with supported early_stop in epoch:
pip install perming>=1.4.2
download version without enhancing Parallel and delayed:
pip install perming==1.6.1
download version with enhancing Parallel and delayed:
pip install perming>=1.7.0
download version with supported set_freeze:
pip install perming>=1.8.0
download version without crash of jupyter kernel:
pip install perming>=1.8.1
download version with optimized _val_acc (avoid CUDA out of memory which may occured from 1.7.* to 1.9.1)
pip install perming==1.9.2
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
perming-1.9.3-py3-none-any.whl
(16.4 kB
view hashes)
