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data_loader.py
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data_loader.py
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from warnings import warn
import numpy as np
import torch
class DataLoader:
def __init__(self, data, target, batch_size, nn_input_shape, e_exp=None, weight=None, train_param=None):
if not (isinstance(data, np.ndarray) and isinstance(target, np.ndarray)):
raise ValueError('Error: data or target is not numpy.ndarray.')
if not (data.ndim == 4 and (target.ndim == 2 or target.ndim == 4)):
raise ValueError('Error: data should be four-dimensional and target should be two-dimensional.')
if data.shape[0] != target.shape[0]:
raise ValueError('Error: The numbers of samples in data and target should be the same.')
if batch_size > data.shape[0]:
warn('batch_size is larger than the sample size, so DataLoader forces batch_size to be the sample size.')
batch_size = data.shape[0]
if not (data.dtype == 'float32' and target.dtype == 'float32'):
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.data = data.astype('float32')
self.target = target.astype('float32')
else:
self.data = data
self.target = target
if e_exp is None:
self.e_exp = np.ones([self.data.shape[0], 1], dtype='float32')
else:
if not isinstance(e_exp, np.ndarray):
raise ValueError('Error: e_exp is not numpy.ndarray.')
if not e_exp.ndim == 2:
raise ValueError('Error: e_exp should be a two-dimensional column vector.')
if e_exp.dtype == 'float32':
self.e_exp = e_exp
else:
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.e_exp = e_exp.astype('float32')
if weight is None:
self.weight = np.ones([self.data.shape[0], 1], dtype='float32')
else:
if not isinstance(weight, np.ndarray):
raise ValueError('Error: weight is not numpy.ndarray.')
if not weight.ndim == 2:
raise ValueError('Error: weight should be a two-dimensional column vector.')
if weight.dtype == 'float32':
self.weight = weight
else:
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.weight = weight.astype('float32')
self.batch_size = int(batch_size)
self.original_sample_n = data.shape[0]
self.original_sample_index_list = range(self.original_sample_n)
self.total_batch_n = self.original_sample_n / self.batch_size
self.total_sample_n = self.total_batch_n * self.batch_size
self.total_sample_index_list = self._reset_total_sample_index_list(shuffle=False)
self.current_batch_idx = -1
self.sample_idx_in_current_batch_list = []
self.used_sample_index_list = []
self.margin = np.zeros([self.original_sample_n] + nn_input_shape, dtype='float32')
def update_margin(self, margin_update, epoch_idx=None, link=None, bnn=None):
"""
Update the margin.
:param torch.FloatTensor margin_update: [self.data.shape[0]] + nn_input_shape
:param link: link function
"""
self.margin += margin_update.cpu().numpy()
def next_batch(self):
"""
Get a batch.
:return: a boolean value whether the current batch is the last one
"""
if self.current_batch_idx < self.total_batch_n - 1:
self.current_batch_idx += 1
self.sample_idx_in_current_batch_list = self.total_sample_index_list[
(self.current_batch_idx * self.batch_size):
(self.current_batch_idx + 1) * self.batch_size
]
self.used_sample_index_list.extend(self.sample_idx_in_current_batch_list)
return True
else:
self.sample_idx_in_current_batch_list = []
return False
def get_data_for_nn(self, enable_cuda):
if enable_cuda:
return torch.from_numpy(self.margin[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.target[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.e_exp[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.weight[self.sample_idx_in_current_batch_list]).cuda(),
else:
return torch.from_numpy(self.margin[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.target[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.e_exp[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.weight[self.sample_idx_in_current_batch_list]),
def get_data_for_booster_layer(self, enable_cuda):
if enable_cuda:
return torch.from_numpy(self.data[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.margin[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.target[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.e_exp[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.weight[self.used_sample_index_list]).cuda()
else:
return torch.from_numpy(self.data[self.used_sample_index_list]), \
torch.from_numpy(self.margin[self.used_sample_index_list]), \
torch.from_numpy(self.target[self.used_sample_index_list]), \
torch.from_numpy(self.e_exp[self.used_sample_index_list]), \
torch.from_numpy(self.weight[self.used_sample_index_list])
def get_length_of_data_for_booster_layer(self):
return self.used_sample_index_list.__len__()
def reset_used_data(self):
self.used_sample_index_list = []
def start_new_round(self, shuffle=True):
self.current_batch_idx = -1
self.total_sample_index_list = self._reset_total_sample_index_list(shuffle=shuffle)
self.sample_idx_in_current_batch_list = []
self.used_sample_index_list = []
def _reset_total_sample_index_list(self, shuffle=True):
if shuffle:
return np.random.permutation(self.original_sample_index_list)[:self.total_sample_n].tolist()
else:
return self.original_sample_index_list[:self.total_sample_n]
class ProbWeightedDataLoader:
def __init__(self, data, target, batch_size, nn_input_shape, e_exp=None, weight=None, train_param=None):
if not (isinstance(data, np.ndarray) and isinstance(target, np.ndarray)):
raise ValueError('Error: data or target is not numpy.ndarray.')
if not (data.ndim == 4 and (target.ndim == 2 or target.ndim == 4)):
raise ValueError('Error: data should be four-dimensional and target should be two-dimensional.')
if data.shape[0] != target.shape[0]:
raise ValueError('Error: The numbers of samples in data and target should be the same.')
if batch_size > data.shape[0]:
warn('batch_size is larger than the sample size, so DataLoader forces batch_size to be the sample size.')
batch_size = data.shape[0]
if not (data.dtype == 'float32' and target.dtype == 'float32'):
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.data = data.astype('float32')
self.target = target.astype('float32')
else:
self.data = data
self.target = target
if e_exp is None:
self.e_exp = np.ones([self.data.shape[0], 1], dtype='float32')
else:
if not isinstance(e_exp, np.ndarray):
raise ValueError('Error: e_exp is not numpy.ndarray.')
if not e_exp.ndim == 2:
raise ValueError('Error: e_exp should be a two-dimensional column vector.')
if e_exp.dtype == 'float32':
self.e_exp = e_exp
else:
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.e_exp = e_exp.astype('float32')
if weight is None:
self.weight = np.ones([self.data.shape[0], 1], dtype='float32')
else:
if not isinstance(weight, np.ndarray):
raise ValueError('Error: weight is not numpy.ndarray.')
if not weight.ndim == 2:
raise ValueError('Error: weight should be a two-dimensional column vector.')
if weight.dtype == 'float32':
self.weight = weight
else:
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.weight = weight.astype('float32')
self.batch_size = int(batch_size)
self.original_sample_n = data.shape[0]
self.original_sample_index_list = range(self.original_sample_n)
self.total_batch_n = self.original_sample_n / self.batch_size
self.total_sample_n = self.total_batch_n * self.batch_size
self.total_sample_index_list = self._reset_total_sample_index_list(shuffle=False)
self.current_batch_idx = -1
self.sample_idx_in_current_batch_list = []
self.used_sample_index_list = []
self.margin = np.zeros([self.original_sample_n] + nn_input_shape, dtype='float32')
# weighting related code
if 'use_prob_as_weight_after_n_epoch' in train_param:
self.use_prob_as_weight_after_n_epoch = train_param['use_prob_as_weight_after_n_epoch']
else:
self.use_prob_as_weight_after_n_epoch = 0
if 'power_of_prob_as_weight' in train_param:
self.power_of_prob_as_weight = train_param['power_of_prob_as_weight']
else:
self.power_of_prob_as_weight = 1.0
if 'coef_of_prob_as_weight' in train_param:
self.coef_of_prob_as_weight = train_param['coef_of_prob_as_weight']
else:
self.coef_of_prob_as_weight = 1.0
def update_margin(self, margin_update, epoch_idx, link, bnn):
"""
Update the margin.
:param torch.FloatTensor margin_update: [self.data.shape[0]] + nn_input_shape
:param int epoch_idx: the index of the current epoch
:param link: link function
:param bnn: bnn instance
"""
self.margin += margin_update.cpu().numpy()
if epoch_idx > self.use_prob_as_weight_after_n_epoch:
self.weight = (
self.coef_of_prob_as_weight
*
link(
bnn.nn_forward(
torch.from_numpy(self.margin),
requires_grad=False
).data,
torch.from_numpy(self.e_exp)
).numpy()
**
self.power_of_prob_as_weight
)
# self.weight = (
# self.coef_of_prob_as_weight
# *
# np.exp(
# bnn.nn_forward(
# torch.from_numpy(self.margin),
# requires_grad=False
# ).data.numpy()
# )
# **
# self.power_of_prob_as_weight
# )
def next_batch(self):
"""
Get a batch.
:return: a boolean value whether the current batch is the last one
"""
if self.current_batch_idx < self.total_batch_n - 1:
self.current_batch_idx += 1
self.sample_idx_in_current_batch_list = self.total_sample_index_list[
(self.current_batch_idx * self.batch_size):
(self.current_batch_idx + 1) * self.batch_size
]
self.used_sample_index_list.extend(self.sample_idx_in_current_batch_list)
return True
else:
self.sample_idx_in_current_batch_list = []
return False
def get_data_for_nn(self, enable_cuda):
if enable_cuda:
return torch.from_numpy(self.margin[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.target[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.e_exp[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.weight[self.sample_idx_in_current_batch_list]).cuda(),
else:
return torch.from_numpy(self.margin[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.target[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.e_exp[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.weight[self.sample_idx_in_current_batch_list]),
def get_data_for_booster_layer(self, enable_cuda):
if enable_cuda:
return torch.from_numpy(self.data[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.margin[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.target[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.e_exp[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.weight[self.used_sample_index_list]).cuda()
else:
return torch.from_numpy(self.data[self.used_sample_index_list]), \
torch.from_numpy(self.margin[self.used_sample_index_list]), \
torch.from_numpy(self.target[self.used_sample_index_list]), \
torch.from_numpy(self.e_exp[self.used_sample_index_list]), \
torch.from_numpy(self.weight[self.used_sample_index_list])
def get_length_of_data_for_booster_layer(self):
return self.used_sample_index_list.__len__()
def reset_used_data(self):
self.used_sample_index_list = []
def start_new_round(self, shuffle=True):
self.current_batch_idx = -1
self.total_sample_index_list = self._reset_total_sample_index_list(shuffle=shuffle)
self.sample_idx_in_current_batch_list = []
self.used_sample_index_list = []
def _reset_total_sample_index_list(self, shuffle=True):
if shuffle:
return np.random.permutation(self.original_sample_index_list)[:self.total_sample_n].tolist()
else:
return self.original_sample_index_list[:self.total_sample_n]
class IncWeightedDataLoader:
def __init__(self, data, target, batch_size, nn_input_shape, e_exp=None, weight=None, train_param=None):
if not (isinstance(data, np.ndarray) and isinstance(target, np.ndarray)):
raise ValueError('Error: data or target is not numpy.ndarray.')
if not (data.ndim == 4 and (target.ndim == 2 or target.ndim == 4)):
raise ValueError('Error: data should be four-dimensional and target should be two-dimensional.')
if data.shape[0] != target.shape[0]:
raise ValueError('Error: The numbers of samples in data and target should be the same.')
if batch_size > data.shape[0]:
warn('batch_size is larger than the sample size, so DataLoader forces batch_size to be the sample size.')
batch_size = data.shape[0]
if not (data.dtype == 'float32' and target.dtype == 'float32'):
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.data = data.astype('float32')
self.target = target.astype('float32')
else:
self.data = data
self.target = target
if e_exp is None:
self.e_exp = np.ones([self.data.shape[0], 1], dtype='float32')
else:
if not isinstance(e_exp, np.ndarray):
raise ValueError('Error: e_exp is not numpy.ndarray.')
if not e_exp.ndim == 2:
raise ValueError('Error: e_exp should be a two-dimensional column vector.')
if e_exp.dtype == 'float32':
self.e_exp = e_exp
else:
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.e_exp = e_exp.astype('float32')
if weight is None:
self.weight = np.ones([self.data.shape[0], 1], dtype='float32')
else:
if not isinstance(weight, np.ndarray):
raise ValueError('Error: weight is not numpy.ndarray.')
if not weight.ndim == 2:
raise ValueError('Error: weight should be a two-dimensional column vector.')
if weight.dtype == 'float32':
self.weight = weight
else:
warn('The data type of data and target should be float 32. DataLoader has converted them to float32.')
self.weight = weight.astype('float32')
self.batch_size = int(batch_size)
self.original_sample_n = data.shape[0]
self.original_sample_index_list = range(self.original_sample_n)
self.total_batch_n = self.original_sample_n / self.batch_size
self.total_sample_n = self.total_batch_n * self.batch_size
self.total_sample_index_list = self._reset_total_sample_index_list(shuffle=False)
self.current_batch_idx = -1
self.sample_idx_in_current_batch_list = []
self.used_sample_index_list = []
self.margin = np.zeros([self.original_sample_n] + nn_input_shape, dtype='float32')
# weight related code
if 'inc_weight_after_n_epoch' in train_param:
self.inc_weight_after_n_epoch = train_param['inc_weight_after_n_epoch']
else:
self.inc_weight_after_n_epoch = 0
if 'inc_coef' in train_param:
self.inc_coef = train_param['inc_coef']
else:
self.inc_coef = 1e-5
def update_margin(self, margin_update, epoch_idx, link=None, bnn=None):
"""
Update the margin.
:param torch.FloatTensor margin_update: [self.data.shape[0]] + nn_input_shape
:param int epoch_idx: the index of the current epoch
:param link: link function
:param bnn: bnn instance
"""
self.margin += margin_update.cpu().numpy()
if epoch_idx > self.inc_weight_after_n_epoch:
self.weight += self.target * self.inc_coef
def next_batch(self):
"""
Get a batch.
:return: a boolean value whether the current batch is the last one
"""
if self.current_batch_idx < self.total_batch_n - 1:
self.current_batch_idx += 1
self.sample_idx_in_current_batch_list = self.total_sample_index_list[
(self.current_batch_idx * self.batch_size):
(self.current_batch_idx + 1) * self.batch_size
]
self.used_sample_index_list.extend(self.sample_idx_in_current_batch_list)
return True
else:
self.sample_idx_in_current_batch_list = []
return False
def get_data_for_nn(self, enable_cuda):
if enable_cuda:
return torch.from_numpy(self.margin[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.target[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.e_exp[self.sample_idx_in_current_batch_list]).cuda(), \
torch.from_numpy(self.weight[self.sample_idx_in_current_batch_list]).cuda(),
else:
return torch.from_numpy(self.margin[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.target[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.e_exp[self.sample_idx_in_current_batch_list]), \
torch.from_numpy(self.weight[self.sample_idx_in_current_batch_list]),
def get_data_for_booster_layer(self, enable_cuda):
if enable_cuda:
return torch.from_numpy(self.data[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.margin[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.target[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.e_exp[self.used_sample_index_list]).cuda(), \
torch.from_numpy(self.weight[self.used_sample_index_list]).cuda()
else:
return torch.from_numpy(self.data[self.used_sample_index_list]), \
torch.from_numpy(self.margin[self.used_sample_index_list]), \
torch.from_numpy(self.target[self.used_sample_index_list]), \
torch.from_numpy(self.e_exp[self.used_sample_index_list]), \
torch.from_numpy(self.weight[self.used_sample_index_list])
def get_length_of_data_for_booster_layer(self):
return self.used_sample_index_list.__len__()
def reset_used_data(self):
self.used_sample_index_list = []
def start_new_round(self, shuffle=True):
self.current_batch_idx = -1
self.total_sample_index_list = self._reset_total_sample_index_list(shuffle=shuffle)
self.sample_idx_in_current_batch_list = []
self.used_sample_index_list = []
def _reset_total_sample_index_list(self, shuffle=True):
if shuffle:
return np.random.permutation(self.original_sample_index_list)[:self.total_sample_n].tolist()
else:
return self.original_sample_index_list[:self.total_sample_n]