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
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Metadata
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Release files for perming 1.9.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| perming-1.9.3-py3-none-any.whl | Python 3 | none | any | Details |
Release files / perming-1.9.3-py3-none-any.whl
| Download URL | perming-1.9.3-py3-none-any.whl |
|---|---|
| Size | 16.4 kB |
| Tags | Python 3 |
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