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common.py
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common.py
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# Copyright (c) 2023 linjing-lab
from ._typing import Tuple, Optional
from ._utils import BaseModel, torch
class Regressier(BaseModel):
'''
Supervised Learning Regressier for Tabular Data.
:param input_: int, input dataset with features' dimension of tabular data is input_.
:param hidden_layer_sizes: Tuple[int], configure the size of each hidden layer. default: (100,).
:param activation: str, configure function that activates the hidden layer. default: relu.
:param criterion: str, loss function determined by different learning problem. default: MSELoss.
:param solver: str, optimization function initialized with `learning_rate_init`. default: adam.
:param batch_size: int, batch size of dataset in one training and validation process. default: 32.
:param learning_rate_init: float, initialize the learning rate of the optimizer. default: 1e-2.
:param lr_scheduler: str | None, set the learning rate scheduler integrated with optimizer. default: None.
'''
def __init__(self,
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) -> None:
super(Regressier, self).__init__(input_,
1,
hidden_layer_sizes,
torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
self._activate(activation),
self._criterion(criterion),
solver,
batch_size,
learning_rate_init,
lr_scheduler)
def _activate(self, activation: str):
'''
Configure Activation with `activation` and `inplace=True`.
:param activation: str, 'relu', 'rrelu', 'leaky_relu', 'elu', 'celu'. default: 'relu'.
'''
if activation == 'relu':
return torch.nn.ReLU(inplace=True)
elif activation == 'rrelu':
return torch.nn.RReLU(inplace=True)
elif activation == 'leaky_relu':
return torch.nn.LeakyReLU(inplace=True)
elif activation == 'elu':
return torch.nn.ELU(inplace=True)
elif activation == 'celu':
return torch.nn.CELU(inplace=True)
else:
raise ValueError("Activation Function Supports Options: relu, rrelu, leaky_relu, elu, celu.")
def _criterion(self, criterion: str):
'''
Configure Loss Criterion with `criterion`.
:param criterion: str, 'MSELoss', 'L1Loss', 'SmoothL1Loss', 'KLDivLoss'. default: MSELoss.
'''
if criterion == 'MSELoss':
return torch.nn.MSELoss()
elif criterion == 'L1Loss':
return torch.nn.L1Loss()
elif criterion == 'SmoothL1Loss':
return torch.nn.SmoothL1Loss()
elif criterion == 'KLDivLoss':
return torch.nn.KLDivLoss()
else:
raise ValueError("Criterion Configuration Supports Options: MSELoss, L1Loss, SmoothL1Loss, KLDivLoss.")
class Binarier(BaseModel):
'''
Binary Supervised Learning Classifier for Tabular Data.
:param input_: int, input dataset with features' dimension of tabular data is input_.
:param hidden_layer_sizes: Tuple[int], configure the size of each hidden layer. default: (100,).
:param activation: str, configure function that activates the hidden layer. default: relu.
:param criterion: str, loss function determined by different learning problem. default: CrossEntropyLoss.
:param solver: str, optimization function coordinated with `torch.optim.lr_scheduler`. default: adam.
:param batch_size: int, batch size of dataset in one training and validation process. default: 32.
:param learning_rate_init: float, initialize the learning rate of the optimizer. default: 1e-2.
:param lr_scheduler: str | None, set the learning rate scheduler integrated with optimizer. default: None.
'''
def __init__(self,
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) -> None:
super(Binarier, self).__init__(input_,
2,
hidden_layer_sizes,
torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
self._activate(activation),
self._criterion(criterion),
solver,
batch_size,
learning_rate_init,
lr_scheduler)
def _activate(self, activation: str):
'''
Configure Activation with `activation` and partly `inplace=True`.
:param activation: str, 'relu', 'tanh', 'sigmoid', 'rrelu', 'leaky_relu', 'elu', 'celu'.
'''
if activation == 'relu':
return torch.nn.ReLU(inplace=True)
elif activation == 'tanh':
return torch.nn.Tanh()
elif activation == 'sigmoid':
return torch.nn.Sigmoid()
elif activation == 'rrelu':
return torch.nn.RReLU(inplace=True)
elif activation == 'leaky_relu':
return torch.nn.LeakyReLU(inplace=True)
elif activation == 'elu':
return torch.nn.ELU(inplace=True)
elif activation == 'celu':
return torch.nn.CELU(inplace=True)
else:
raise ValueError("Activation Function Supports Options: relu, tanh, sigmoid, rrelu, leaky_relu, elu, celu.")
def _criterion(self, criterion: str):
'''
Configure Loss Criterion with `criterion`.
:param criterion: str, 'CrossEntropyLoss', 'BCEWithLogitsLoss'. default: CrossEntropyLoss.
'''
if criterion == 'CrossEntropyLoss':
return torch.nn.CrossEntropyLoss()
elif criterion == 'BCEWithLogitsLoss': # ! target and input need to be same size when adopt
return torch.nn.BCEWithLogitsLoss()
else:
raise ValueError("Criterion Configuration Supports Options: CrossEntropyLoss, BCEWithLogitsLoss.")
class Mutipler(BaseModel):
'''
Mutiple Supervised Learning Classifier for Tabular Data.
:param input_: int, input dataset with features' dimension of tabular data is input_.
:param num_classes: int, total number of correct label categories.
:param hidden_layer_sizes: Tuple[int], configure the size of each hidden layer. default: (100,).
:param activation: str, configure function that activates the hidden layer. default: relu.
:param criterion: str, loss function determined by different learning problem. default: CrossEntropyLoss.
:param solver: str, optimization function coordinated with `torch.optim.lr_scheduler`. default: adam.
:param batch_size: int, batch size of dataset in one training and validation process. default: 32.
:param learning_rate_init: float, initialize the learning rate of the optimizer. default: 1e-2.
:param lr_scheduler: str | None, set the learning rate scheduler integrated with optimizer. default: None.
'''
def __init__(self,
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) -> None:
super(Mutipler, self).__init__(input_,
num_classes,
hidden_layer_sizes,
torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
self._activate(activation),
self._criterion(criterion),
solver,
batch_size,
learning_rate_init,
lr_scheduler)
assert num_classes >= 2
def _activate(self, activation: str):
'''
Configure Activation with `activation` and `inplace=True`.
:param activation: str, 'relu', 'rrelu', 'leaky_relu', 'elu', 'celu'.
'''
if activation == 'relu':
return torch.nn.ReLU(inplace=True)
elif activation == 'rrelu':
return torch.nn.RReLU(inplace=True)
elif activation == 'leaky_relu':
return torch.nn.LeakyReLU(inplace=True)
elif activation == 'elu':
return torch.nn.ELU(inplace=True)
elif activation == 'celu':
return torch.nn.CELU(inplace=True)
else:
raise ValueError("Activation Function Supports Options: relu, rrelu, leaky_relu, elu, celu.")
def _criterion(self, criterion: str):
'''
Configure Loss Criterion with `criterion`.
:param criterion: str, 'CrossEntropyLoss', 'NLLLoss'.
'''
if criterion == 'CrossEntropyLoss':
return torch.nn.CrossEntropyLoss()
elif criterion == 'NLLLoss':
return torch.nn.NLLLoss()
else:
raise ValueError("Criterion Configuration Supports Options: CrossEntropyLoss, NLLLoss.")
class Ranker(BaseModel):
'''
Supervised Learning Outputs Ranker for Tabular Data.
:param input_: int, input dataset with features' dimension of tabular data is input_.
:param num_outputs: int, total number of correct label outputs.
:param hidden_layer_sizes: Tuple[int], configure the size of each hidden layer. default: (100,).
:param activation: str, configure function that activates the hidden layer. default: relu.
:param criterion: str, loss function determined by different learning problem. default: MultiLabelSoftMarginLoss.
:param solver: str, optimization function coordinated with `torch.optim.lr_scheduler`. default: adam.
:param batch_size: int, batch size of dataset in one training and validation process. default: 32.
:param learning_rate_init: float, initialize the learning rate of the optimizer. default: 1e-2.
:param lr_scheduler: str | None, set the learning rate scheduler integrated with optimizer. default: None.
'''
def __init__(self,
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) -> None:
super(Ranker, self).__init__(input_,
num_outputs,
hidden_layer_sizes,
torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
self._activate(activation),
self._criterion(criterion),
solver,
batch_size,
learning_rate_init,
lr_scheduler)
def _activate(self, activation: str):
'''
Configure Activation with `activation` and `inplace=True`.
:param activation: str, 'relu', 'rrelu', 'leaky_relu', 'elu', 'celu'.
'''
if activation == 'relu':
return torch.nn.ReLU(inplace=True)
elif activation == 'rrelu':
return torch.nn.RReLU(inplace=True)
elif activation == 'leaky_relu':
return torch.nn.LeakyReLU(inplace=True)
elif activation == 'elu':
return torch.nn.ELU(inplace=True)
elif activation == 'celu':
return torch.nn.CELU(inplace=True)
else:
raise ValueError("Activation Function Supports Options: relu, rrelu, leaky_relu, elu, celu.")
def _criterion(self, criterion: str):
'''
Configure Loss Criterion with `criterion`.
:param criterion: str, 'MultiLabelMarginLoss', 'BCEWithLogitsLoss', 'MSELoss'. default: MultiLabelMarginLoss
'''
if criterion == 'MultiLabelSoftMarginLoss': # torch.long
return torch.nn.MultiLabelSoftMarginLoss()
elif criterion == 'BCEWithLogitsLoss': # torch.float
return torch.nn.BCEWithLogitsLoss()
elif criterion == 'MSELoss': # torch.float
return torch.nn.MSELoss()
else:
raise ValueError("Criterion Configuration Supports Options: MultiLabelSoftMarginLoss, BCEWithLogitsLoss, MSELoss.")