Source code for

import numpy as np
import pandas as pd
from typing import List, Optional, Union, Tuple
import math

from ...utils.misc import to_device

from torch.tensor import Tensor

__all__ = ['BatchYielder']

- Improve this/change to dataloader

[docs]class BatchYielder: r''' Yields minibatches to model during training. Iteration provides one minibatch as tuple of tensors of inputs, targets, and weights. Arguments: inputs: input array for (sub-)epoch targets: target array for (sub-)epoch bs: batchsize, number of data to include per minibatch objective: 'classification', 'multiclass classification', or 'regression'. Used for casting target dtype. weights: Optional weight array for (sub-)epoch shuffle: whether to shuffle the data at the beginning of an iteration use_weights: if passed weights, whether to actually pass them to the model bulk_move: whether to move all data to device at once. Default is true (saves time), but if device has low memory you can set to False. input_mask: optionally only use Boolean-masked inputs drop_last: whether to drop the last batch if it does not contain `bs` elements ''' def __init__(self, inputs:Union[np.ndarray,Tuple[np.ndarray,np.ndarray]], bs:int, objective:str, targets:Optional[np.ndarray]=None, weights:Optional[np.ndarray]=None, shuffle:bool=True, use_weights:bool=True, bulk_move:bool=True, input_mask:Optional[np.ndarray]=None, drop_last:bool=True): self.inputs,self.targets,self.weights,,self.objective,self.shuffle,self.use_weights,self.bulk_move,self.input_mask,self.drop_last = \ inputs,targets,weights,bs,objective,shuffle,use_weights,bulk_move,input_mask,drop_last if isinstance(self.inputs, tuple): self.inputs,self.matrix_inputs = self.inputs else: self.matrix_inputs = None if isinstance(self.inputs, pd.DataFrame): self.inputs = self.inputs.values if self.input_mask is not None: self.inputs = self.inputs[:,self.input_mask] def __iter__(self) -> List[Tensor]: r''' Iterate through data in batches. Returns: tuple of batches of inputs, targets, and weights as tensors on device ''' full_idxs = np.arange(len(self.inputs)) upper = len(full_idxs) if self.drop_last: upper -= if self.shuffle: np.random.shuffle(full_idxs) if self.bulk_move: inputs = to_device(Tensor(self.inputs)) if self.targets is not None: if 'multiclass' in self.objective: targets = to_device(Tensor(self.targets).long().squeeze()) else: targets = to_device(Tensor(self.targets)) if self.weights is not None and self.use_weights: weights = to_device(Tensor(self.weights)) else: weights = None if self.matrix_inputs is not None: matrix_inputs = to_device(Tensor(self.matrix_inputs)) else: matrix_inputs = None for i in range(0, upper, idxs = full_idxs[] x = inputs[idxs] if matrix_inputs is None else (inputs[idxs],matrix_inputs[idxs]) y = None if self.targets is None else targets[idxs] w = None if weights is None else weights[idxs] yield x, y, w else: for i in range(0, upper, idxs = full_idxs[] if self.targets is not None: if 'multiclass' in self.objective: y = to_device(Tensor(self.targets[idxs]).long().squeeze()) else: y = to_device(Tensor(self.targets[idxs])) else: y = None if self.matrix_inputs is None: x = to_device(Tensor(self.inputs[idxs])) else: x = (to_device(Tensor(self.inputs[idxs])),to_device(Tensor(self.matrix_inputs[idxs]))) w = to_device(Tensor(self.weights[idxs])) if self.weights is not None and self.use_weights else None yield x, y, w def __len__(self): return len(self.inputs)// if self.drop_last else math.ceil(len(self.inputs)/
[docs] def get_inputs(self, on_device:bool=False) -> Union[Tensor, Tuple[Tensor,Tensor]]: if on_device: if self.matrix_inputs is None: return to_device(Tensor(self.inputs)) else: return (to_device(Tensor(self.inputs)), to_device(Tensor(self.matrix_inputs))) else: if self.matrix_inputs is None: return self.inputs else: return (self.inputs, self.matrix_inputs)
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