repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
TEXTOIR | TEXTOIR-main/open_intent_detection/losses/ARPLoss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .Dist import Dist
class ARPLoss(nn.CrossEntropyLoss):
def __init__(self, args):
super(ARPLoss, self).__init__()
self.weight_pl = float(args.weight_pl)
self.device = args.device
self.temp = args.temp
self... | 2,241 | 35.16129 | 94 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/losses/CosineFaceLoss.py | import torch
import math
import torch.nn.functional as F
from torch import nn
from torch.nn.parameter import Parameter
class CosineFaceLoss(nn.Module):
"""
cos_theta need to be normalized first
"""
def __init__(self, m=0.35, s=30):
super(CosineFaceLoss, self).__init__()
self... | 677 | 24.111111 | 62 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/losses/__init__.py | from .CosineFaceLoss import CosineFaceLoss
from torch import nn
loss_map = {
'CrossEntropyLoss': nn.CrossEntropyLoss(),
'Binary_CrossEntropyLoss': nn.BCELoss(),
'CosineFaceLoss': CosineFaceLoss()
}
| 261 | 28.111111 | 59 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/backbones/bert.py | import torch
import math
import torch.nn.functional as F
import numpy as np
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from torch.nn.parameter import Parameter
from transformers import BertPreTrainedModel, BertModel, BertForMaskedLM, AutoConfig
from transformers.modeling_outputs import Sequenc... | 24,277 | 37.8448 | 125 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/backbones/base.py | import torch
import logging
from transformers import AdamW, get_linear_schedule_with_warmup
from .utils import freeze_bert_parameters, freeze_bert_parameters_KCL
from .__init__ import backbones_map
class ModelManager:
def __init__(self, args, data, logger_name = 'Detection'):
self.logger = loggin... | 2,314 | 43.519231 | 118 | py |
TEXTOIR | TEXTOIR-main/open_intent_detection/backbones/utils.py | import torch
from torch import nn
import numpy as np
def l2_norm(input,axis=1):
norm = torch.norm(input, 2, axis, True)
output = torch.div(input, norm)
return output
class L2_normalization(nn.Module):
def forward(self, input):
return l2_norm(input)
def freeze_bert_parameters(model):
fo... | 4,408 | 47.450549 | 164 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/DTC_BERT/pretrain.py | import logging
import torch
import numpy as np
import os
import copy
import logging
import torch.nn.functional as F
import pandas as pd
import random
import math
from sklearn.metrics import silhouette_score
from sklearn.metrics import accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.... | 14,030 | 40.389381 | 177 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/DTC_BERT/manager.py | import logging
import copy
import os
import random
import torch
import torch.nn.functional as F
import numpy as np
import math
import pandas as pd
from .pretrain import PretrainDTCManager
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from utils.metrics im... | 10,610 | 38.741573 | 152 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/USNID/pretrain.py | from turtle import distance
import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
import time
from sklearn.metrics import accuracy_score
from tqdm import trange, tqdm
from itertools import cycle
from losses import loss_map
from utils.functions import save_model, restore_m... | 11,347 | 39.967509 | 162 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/USNID/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import logging
import os
import time
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model
from torch.utils.data imp... | 14,574 | 43.571865 | 158 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/MCL_BERT/manager.py | import torch
import logging
import copy
import torch.nn.functional as F
from tqdm import trange, tqdm
from sklearn.metrics import confusion_matrix
from losses import loss_map
from utils.metrics import clustering_score
from utils.functions import restore_model, save_model
class MCLManager:
def __init__(self,... | 5,195 | 33.184211 | 134 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/DeepAligned/pretrain.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
from sklearn.metrics import accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model
from sklearn.cluster import KMeans
class PretrainDeepAlignedM... | 6,507 | 34.562842 | 143 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/DeepAligned/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import copy
import logging
import os
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score, confusion_matrix
from tqdm import trange, tqdm
from scipy.optimize import linear_sum_assignment
from losses import loss_map
from utils.fu... | 9,427 | 37.798354 | 134 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/GCD/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import copy
import logging
import pandas as pd
from sklearn.cluster import KMeans
from utils.metrics import clustering_score
from sklearn.metrics import accuracy_score, confusion_matrix
from tqdm import trange, tqdm
from torch.utils.data import ... | 11,876 | 42.988889 | 153 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/CDACPlus/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import copy
import logging
from sklearn.metrics import confusion_matrix
from sklearn.cluster import KMeans
from tqdm import trange, tqdm
from utils.functions import set_seed
from utils.metrics import clustering_score
from utils.functions import restore_mod... | 10,297 | 37.425373 | 154 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/KCL_BERT/pretrain.py | import logging
import numpy as np
import copy
import torch
import os
import torch.nn.functional as F
from losses import loss_map
from tqdm import tqdm, trange
from sklearn.metrics import accuracy_score
from utils.functions import save_model, set_seed
class PretrainKCLManager:
def __init__(self, args, data, ... | 5,631 | 35.571429 | 142 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/KCL_BERT/manager.py | import logging
import torch
import os
import copy
import torch.nn.functional as F
from tqdm import trange, tqdm
from sklearn.metrics import confusion_matrix
from losses import loss_map
from .pretrain import PretrainKCLManager
from utils.functions import restore_model, save_model, set_seed
from utils.metrics import clu... | 6,349 | 33.89011 | 135 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/MTP_CLNN/pretrain.py | import torch
import torch.nn.functional as F
import os
import copy
import logging
import torch.nn as nn
from sklearn.metrics import accuracy_score
from tqdm import trange, tqdm
from losses import loss_map
from torch.utils.data import RandomSampler, DataLoader
from utils.functions import save_model, mask_tokens, set_se... | 7,385 | 39.80663 | 173 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/semi_supervised/MTP_CLNN/manager.py | import torch
import torch.nn.functional as F
import logging
import os
import torch.nn as nn
import numpy as np
import copy
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from losses import loss_map
from utils.functions import save_model, restore_model, Mem... | 8,780 | 41.626214 | 167 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/unsupervised/DEC/manager.py | import logging
import os
import numpy as np
import copy
from utils.metrics import clustering_score
from sklearn.metrics import confusion_matrix
from keras.models import Model
from keras.optimizers import SGD
from tqdm import trange
from configs.base import ParamManager
from utils.functions import set_seed
from backbone... | 5,599 | 39 | 145 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/unsupervised/USNID/pretrain.py | import torch
import torch.nn.functional as F
import numpy as np
import os
import logging
import time
from torch.utils.data import DataLoader, TensorDataset, RandomSampler
from tqdm import trange, tqdm
from transformers import BertTokenizer
from losses import loss_map
from utils.functions import save_model, restore_mod... | 7,235 | 38.540984 | 142 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/unsupervised/USNID/manager.py | import torch
import torch.nn.functional as F
import numpy as np
import logging
import os
import time
from torch.utils.data import DataLoader, TensorDataset, RandomSampler
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from scipy.optimize import linear_sum... | 11,846 | 41.768953 | 158 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/unsupervised/DCN/manager.py | import logging
import os
import numpy as np
import copy
from sklearn.metrics import confusion_matrix
from keras.models import Model
from keras.optimizers import SGD
from tqdm import trange
from configs.base import ParamManager
from utils.metrics import clustering_score
from utils.functions import set_seed
from backbon... | 5,779 | 39.704225 | 145 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/unsupervised/SCCL/manager.py | import logging
import numpy as np
import copy
import torch
import torch.nn as nn
from utils.metrics import clustering_score
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from sklearn.cluster import KMeans
from torch.utils.data import (DataLoader, RandomSampler, TensorDataset)
from sklearn.... | 9,000 | 40.100457 | 185 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/methods/unsupervised/CC/manager.py | import logging
import numpy as np
import torch
import torch.nn as nn
from utils.metrics import clustering_score
from sklearn.metrics import confusion_matrix
from tqdm import trange, tqdm
from sklearn.cluster import KMeans
from torch.utils.data import (DataLoader, RandomSampler, TensorDataset)
from utils.functions impor... | 6,414 | 40.121795 | 134 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/configs/DCN.py | import os
class Param():
def __init__(self, args):
self.hyper_param = self.get_hyper_parameters(args)
def get_hyper_parameters(self, args):
"""
Args:
num_train_epochs_SAE (int): The number of epochs for training stacked auto-encoder.
num_train_epoch... | 1,284 | 36.794118 | 95 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/configs/DEC.py | import os
class Param():
def __init__(self, args):
self.hyper_param = self.get_hyper_parameters(args)
def get_hyper_parameters(self, args):
"""
Args:
num_train_epochs_SAE (int): The number of epochs for training stacked auto-encoder.
num_train_... | 1,289 | 35.857143 | 95 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/dataloaders/bert_loader.py | import random
import numpy as np
import torch
import os
import csv
import sys
import logging
from transformers import BertTokenizer, AutoTokenizer
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
from sentence_transformers import SentenceTransformer
class BERT_Loader:
... | 17,574 | 45.742021 | 201 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/dataloaders/unsup_loader.py | import pandas as pd
import os
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
import nltk
from nltk.tokenize import word_tokenize
class UNSUP_Loa... | 5,063 | 44.214286 | 135 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/dataloaders/__init__.py | from .bert_loader import BERT_Loader
from .unsup_loader import UNSUP_Loader
max_seq_lengths = { 'stackoverflow':45,
'clinc':30,
'banking':55,
'snips': 35,
}
backbone_loader_map = {
... | 6,879 | 72.978495 | 181 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/utils/functions.py | import os
import torch
import numpy as np
import pandas as pd
import random
import copy
import matplotlib.pyplot as plt
import itertools
import torch.nn.functional as F
import tensorflow as tf
from tqdm import tqdm
from transformers import WEIGHTS_NAME, CONFIG_NAME
def set_seed(seed):
random.seed(seed)
np.rand... | 12,824 | 36.390671 | 153 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/utils/neighbor_dataset.py | import torch
import numpy as np
from torch.utils.data import Dataset
class NeighborsDataset(Dataset):
def __init__(self, dataset, indices, num_neighbors=None):
super(NeighborsDataset, self).__init__()
self.dataset = dataset
self.indices = indices
if num_neighbors is not None:
... | 991 | 31 | 76 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/utils/faster_mix_k_means_pytorch.py | import numpy as np
import copy
import random
#from project_utils.cluster_utils import cluster_acc
from sklearn.utils._joblib import Parallel, delayed, effective_n_jobs
from sklearn.utils import check_random_state
import torch
def pairwise_distance(data1, data2, batch_size=None, distance_metric = 'euroc'):
r'''
... | 9,908 | 34.643885 | 115 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/losses/SupConLoss.py | import torch
from torch import nn
class SupConLoss(nn.Module):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR"""
def __init__(self, contrast_mode='all'):
super(SupConLoss, self).__init__()
self.contrast_... | 3,370 | 38.658824 | 91 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/losses/contrastive_loss.py | import torch
import torch.nn as nn
import math
class InstanceLoss(nn.Module):
def __init__(self, batch_size, temperature, device):
super(InstanceLoss, self).__init__()
self.batch_size = batch_size
self.temperature = temperature
self.device = device
self.mask = self.mask_co... | 3,071 | 32.032258 | 82 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/losses/MCL.py | from torch import nn
class MCL(nn.Module):
# Meta Classification Likelihood (MCL)
eps = 1e-7 # Avoid calculating log(0). Use the small value of float16.
def forward(self, prob1, prob2, simi=None):
# simi: 1->similar; -1->dissimilar; 0->unknown(ignore)
assert len(prob1)==len(prob2)... | 592 | 36.0625 | 135 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/losses/KCL.py | from torch import nn
class KLDiv(nn.Module):
# Calculate KL-Divergence
def forward(self, predict, target):
eps = 1e-7
assert predict.ndimension()==2,'Input dimension must be 2'
target = target.detach()
# KL(T||I) = \sum T(logT-logI)
predict += eps
targ... | 1,042 | 29.676471 | 135 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/losses/PairConLoss.py | import torch
from torch import nn
class PairConLoss(nn.Module):
def __init__(self, temperature=0.05):
super(PairConLoss, self).__init__()
self.temperature = temperature
self.eps = 1e-08
def forward(self, features_1, features_2, device):
batch_size = features_1.shape[0]
... | 1,164 | 37.833333 | 185 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/losses/__init__.py | from torch import nn
from .KCL import KCL
from .MCL import MCL
from .SupConLoss import SupConLoss
loss_map = {
'CrossEntropyLoss': nn.CrossEntropyLoss(),
'KCL': KCL(),
'MCL': MCL(),
'SupConLoss': SupConLoss()
}
| 290 | 23.25 | 59 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/backbones/bert.py | from operator import mod
import torch
import torch.nn.functional as F
from torch import nn
from transformers import BertPreTrainedModel, BertModel, AutoModelForMaskedLM, BertForMaskedLM
from torch.nn.parameter import Parameter
from .utils import PairEnum
from sentence_transformers import SentenceTransformer
from losse... | 23,182 | 38.160473 | 130 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/backbones/base.py | import os
import torch
import math
import logging
from transformers import AdamW, get_linear_schedule_with_warmup
from .utils import freeze_bert_parameters, set_allow_growth
from .__init__ import backbones_map
class ModelManager:
def __init__(self, args, data, logger_name = 'Discovery'):
self.l... | 3,142 | 33.538462 | 120 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/backbones/utils.py | import torch
import tensorflow as tf
from torch import nn
def l2_norm(input,axis=1):
norm = torch.norm(input,2,axis,True)
output = torch.div(input, norm)
return output
class L2_normalization(nn.Module):
def forward(self, input):
return l2_norm(input)
def freeze_bert_parameters(model):
... | 1,141 | 27.55 | 59 | py |
TEXTOIR | TEXTOIR-main/open_intent_discovery/backbones/sae.py | from keras.optimizers import Adam
from keras.models import Sequential
from keras.layers import Dense
from keras import backend as K
from keras.engine.topology import Layer, InputSpec
def get_encoded(model, data, nb_layer):
transform = K.function([model.layers[0].input],
[model.layers[nb... | 4,368 | 46.48913 | 120 | py |
maze3d_collaborative | maze3d_collaborative-main/rl_models/networks_discrete.py | import os
import torch
import torch.nn as nn
from torch.distributions import Categorical
import numpy as np
import torch.nn.functional as F
import random
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
"""
https://github.com/EveLIn3/Discrete_SAC_LunarLander/blob/master/sac_discrete.py
"""
de... | 5,586 | 33.701863 | 121 | py |
maze3d_collaborative | maze3d_collaborative-main/rl_models/sac_discrete_agent.py | import torch
import numpy as np
from rl_models.networks_discrete import update_params, Actor, Critic, ReplayBuffer
import torch.nn.functional as F
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class DiscreteSACAgent:
def __init__(self, config=None, alpha=0.0003, beta=0.0003, input_dims=... | 9,616 | 43.317972 | 116 | py |
maze3d_collaborative | maze3d_collaborative-main/rl_models/networks.py | import os
import torch as T
import torch.nn.functional as F
import torch.nn as nn
import torch.optim as optim
from torch.distributions.normal import Normal
import numpy as np
class CriticNetwork(nn.Module):
def __init__(self, beta, input_dims, n_actions, fc1_dims=256, fc2_dims=256,
name='critic',... | 4,729 | 32.076923 | 79 | py |
maze3d_collaborative | maze3d_collaborative-main/rl_models/sac_agent.py | import os
import torch as T
import torch.nn.functional as F
import numpy as np
from rl_models.buffer import ReplayBuffer
from rl_models.networks import ActorNetwork, CriticNetwork, ValueNetwork
if T.cuda.is_available():
print("Using GPU")
else:
print("Using CPU")
class Agent():
def __init__(self, config=... | 7,574 | 42.285714 | 116 | py |
qcor | qcor-master/docs/source/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# XACC documentation build configuration file, created by
# sphinx-quickstart on Tue Aug 29 20:23:35 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autog... | 5,378 | 29.050279 | 79 | py |
EduCDM | EduCDM-main/setup.py | from setuptools import setup, find_packages
test_deps = [
'pytest>=4',
'pytest-cov>=2.6.0',
# 'pytest-flake8==4.0.1',
'pytest-flake8<5.0.0',
'flake8<5.0.0'
]
setup(
name='EduCDM',
version='0.0.13',
extras_require={
'test': test_deps,
},
packages=find_packages(),
ins... | 527 | 21 | 67 | py |
EduCDM | EduCDM-main/examples/DINA/GD/DINA.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import logging
from EduCDM import GDDINA
import torch
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
train_data = pd.read_csv("../../../data/a0910/train.csv")
valid_data = pd.read_csv("../../../data/a0910/valid.csv")
test_data = pd.read_csv("../../..... | 1,620 | 26.474576 | 93 | py |
EduCDM | EduCDM-main/examples/KaNCD/KaNCD.py | # coding: utf-8
# 2023/3/7 @ WangFei
import logging
from EduCDM import KaNCD
import torch
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
import numpy as np
train_data = pd.read_csv("../../data/a0910/train.csv")
valid_data = pd.read_csv("../../data/a0910/valid.csv")
test_data = pd.read_csv(... | 1,972 | 33.614035 | 109 | py |
EduCDM | EduCDM-main/examples/MCD/MCD.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import logging
from EduCDM import MCD
import torch
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
train_data = pd.read_csv("../../data/a0910/train.csv")
valid_data = pd.read_csv("../../data/a0910/valid.csv")
test_data = pd.read_csv("../../data/a0910/... | 1,021 | 24.55 | 74 | py |
EduCDM | EduCDM-main/examples/MIRT/MIRT.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import logging
from EduCDM import MIRT
import torch
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
train_data = pd.read_csv("../../data/a0910/train.csv")
valid_data = pd.read_csv("../../data/a0910/valid.csv")
test_data = pd.read_csv("../../data/a0910... | 1,025 | 24.65 | 74 | py |
EduCDM | EduCDM-main/examples/IRT/GD/IRT.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import logging
from EduCDM import GDIRT
import torch
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
train_data = pd.read_csv("../../../data/a0910/train.csv")
valid_data = pd.read_csv("../../../data/a0910/valid.csv")
test_data = pd.read_csv("../../../... | 1,029 | 24.75 | 74 | py |
EduCDM | EduCDM-main/examples/NCDM/NCDM.py | # coding: utf-8
# 2021/4/1 @ WangFei
import logging
from EduCDM import NCDM
import torch
from torch.utils.data import TensorDataset, DataLoader
import pandas as pd
import numpy as np
train_data = pd.read_csv("../../data/a0910/train.csv")
valid_data = pd.read_csv("../../data/a0910/valid.csv")
test_data = pd.read_csv("... | 1,889 | 32.157895 | 109 | py |
EduCDM | EduCDM-main/EduCDM/DINA/GD/DINA.py | # coding: utf-8
# 2021/6/21 @ tongshiwei
import logging
import numpy as np
import torch
from EduCDM import CDM
from torch import nn
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, accuracy_score
import torch.autograd as autograd
import torch.nn.functional as F
class DINANet(nn.Module):
def __ini... | 5,440 | 37.864286 | 118 | py |
EduCDM | EduCDM-main/EduCDM/ICD/ICD.py | import logging
from EduCDM import CDM
import pandas as pd
from copy import deepcopy
import torch
from baize.torch import Configuration
from baize.torch import light_module as lm
from EduCDM.ICD.etl import transform, user2items, item2users, dict_etl, Dict2
from EduCDM.ICD.sym import eval_f, get_net, DualICD, get_dual_l... | 9,248 | 40.475336 | 110 | py |
EduCDM | EduCDM-main/EduCDM/ICD/etl/etl.py | # coding: utf-8
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
from .utils import pack_batch, multi_hot
from longling import iterwrap
from baize.utils import pad_sequence
class Dict2(object):
def __init__(self):
self.u2i = {}
self.i2u = {}
self.u2i_r_dis = {}
... | 7,490 | 30.082988 | 78 | py |
EduCDM | EduCDM-main/EduCDM/ICD/etl/utils.py | # coding: utf-8
import torch
from baize.utils import pad_sequence
from torch import Tensor, LongTensor
def multi_hot(ks, kn):
array = [0] * kn
for k in ks:
array[k] = 1
return array
def pack_batch(batch):
user_id, user_items, item_id, item_users, item_knows, response = zip(*batch)
user_... | 761 | 28.307692 | 110 | py |
EduCDM | EduCDM-main/EduCDM/ICD/sym/fit_eval.py | # coding: utf-8
import logging
from torch.utils.data import TensorDataset, DataLoader
import math
import pandas as pd
import torch
from tqdm import tqdm
from scipy.stats import entropy
from baize.metrics import classification_report, POrderedDict
from baize.torch import fit_wrapper, eval_wrapper
from longling.ML.Pytor... | 9,592 | 33.260714 | 79 | py |
EduCDM | EduCDM-main/EduCDM/ICD/sym/pos_linear.py | # coding: utf-8
import torch
import torch.nn.functional as F
from torch import nn
class PosLinear(nn.Linear):
def forward(self, input: torch.Tensor) -> torch.Tensor:
weight = 2 * F.relu(1 * torch.neg(self.weight)) + self.weight
return F.linear(input, weight, self.bias)
| 293 | 23.5 | 69 | py |
EduCDM | EduCDM-main/EduCDM/ICD/sym/net/dtn.py | # coding: utf-8
import torch
from torch import nn
from baize.torch.functional import mask_sequence
class DTN(nn.Module):
def __init__(self, input_dim, know_dim):
self.know_dim = know_dim
self.input_dim = input_dim
self.fea_dim = 64
super(DTN, self).__init__()
self.emb = nn... | 1,767 | 33 | 76 | py |
EduCDM | EduCDM-main/EduCDM/ICD/sym/net/ncd.py | # coding: utf-8
import torch
from torch import nn
from ..pos_linear import PosLinear
class NCDMNet(nn.Module):
def __init__(self, trait_dim, know_dim):
super(NCDMNet, self).__init__()
self.knowledge_dim = know_dim
self.prednet_input_len = self.knowledge_dim
self.prednet_len1, self... | 1,650 | 34.891304 | 79 | py |
EduCDM | EduCDM-main/EduCDM/ICD/sym/net/net.py | # coding: utf-8
from tqdm import tqdm
import torch
from torch import nn
from baize.torch import loss_dict2tmt_torch_loss
from longling.ML.PytorchHelper import set_device
from longling.ML.PytorchHelper.toolkit.trainer import collect_params
from .ncd import NCDMNet
from .mirt import MIRTNet
from .dtn import DTN
class... | 6,342 | 33.102151 | 79 | py |
EduCDM | EduCDM-main/EduCDM/ICD/sym/net/mirt.py | # coding: utf-8
import torch
from torch import nn
import torch.nn.functional as F
from .dtn import DTN
from EduCDM.MIRT.MIRT import irt2pl
class MIRTNet(nn.Module):
def __init__(self, trait_dim, a_range=0.1, irf_kwargs=None):
super(MIRTNet, self).__init__()
self.irf_kwargs = irf_kwargs if irf_kwa... | 1,703 | 33.08 | 99 | py |
EduCDM | EduCDM-main/EduCDM/KaNCD/KaNCD.py | # coding: utf-8
# 2023/7/3 @ WangFei
import logging
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, accuracy_score
from EduCDM import CDM
class PosLinear(nn.Linear):
def forward(self... | 7,533 | 44.660606 | 120 | py |
EduCDM | EduCDM-main/EduCDM/MCD/MCD.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import logging
import numpy as np
import torch
from tqdm import tqdm
from torch import nn
from EduCDM import CDM
from sklearn.metrics import roc_auc_score, accuracy_score
class MFNet(nn.Module):
"""Matrix Factorization Network"""
def __init__(self, user_num, item_num... | 3,301 | 36.101124 | 99 | py |
EduCDM | EduCDM-main/EduCDM/MIRT/MIRT.py | # coding: utf-8
# 2021/7/1 @ tongshiwei
import logging
import numpy as np
import torch
from EduCDM import CDM
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, accuracy_score
def irt2pl(theta, a, b, *, F=np):
"""
Parameters
----------
... | 4,314 | 32.710938 | 99 | py |
EduCDM | EduCDM-main/EduCDM/IRT/GD/IRT.py | # coding: utf-8
# 2021/4/23 @ tongshiwei
import logging
import numpy as np
import torch
from EduCDM import CDM
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
from ..irt import irt3pl
from sklearn.metrics import roc_auc_score, accuracy_score
class IRTNet(nn.Module):
def __init__(self, ... | 4,142 | 38.084906 | 112 | py |
EduCDM | EduCDM-main/EduCDM/NCDM/NCDM.py | # coding: utf-8
# 2021/4/1 @ WangFei
import logging
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, accuracy_score
from EduCDM import CDM
class PosLinear(nn.Linear):
def forward(self... | 4,850 | 38.762295 | 97 | py |
EduCDM | EduCDM-main/EduCDM/IRR/DINA.py | # coding: utf-8
# 2021/7/1 @ tongshiwei
import pandas as pd
import numpy as np
import torch
from torch import nn
from EduCDM import GDDINA
from .loss import PairSCELoss, HarmonicLoss, loss_mask
from tqdm import tqdm
from longling.ML.metrics import ranking_report
class DINA(GDDINA):
def __init__(self, user_num, i... | 4,584 | 37.208333 | 108 | py |
EduCDM | EduCDM-main/EduCDM/IRR/IRT.py | # coding: utf-8
# 2021/6/19 @ tongshiwei
import torch
from torch import nn
from tqdm import tqdm
from EduCDM.IRT.GD import IRT as PointIRT
import numpy as np
import pandas as pd
from .loss import PairSCELoss, HarmonicLoss, loss_mask
from longling.ML.metrics import ranking_report
__all__ = ["IRT"]
class IRT(PointIRT... | 4,451 | 35.793388 | 108 | py |
EduCDM | EduCDM-main/EduCDM/IRR/MIRT.py | # coding: utf-8
# 2021/7/1 @ tongshiwei
import torch
from torch import nn
from tqdm import tqdm
from EduCDM import MIRT as PointMIRT
import numpy as np
import pandas as pd
from .loss import PairSCELoss, HarmonicLoss, loss_mask
from longling.ML.metrics import ranking_report
__all__ = ["MIRT"]
class MIRT(PointMIRT):... | 4,552 | 36.01626 | 108 | py |
EduCDM | EduCDM-main/EduCDM/IRR/loss.py | # coding: utf-8
# 2021/6/19 @ tongshiwei
import torch
from torch import nn
def loss_mask(loss_list, n_samples):
return [(i <= n_samples) * loss for i, loss in enumerate(loss_list)]
class PairSCELoss(nn.Module):
def __init__(self):
super(PairSCELoss, self).__init__()
self._loss = nn.CrossEnt... | 999 | 28.411765 | 101 | py |
EduCDM | EduCDM-main/EduCDM/IRR/NCDM.py | # coding: utf-8
# 2021/7/1 @ tongshiwei
import pandas as pd
import numpy as np
import torch
from torch import nn
from EduCDM import NCDM as PointNCDM
from .loss import PairSCELoss, HarmonicLoss, loss_mask
from tqdm import tqdm
from longling.ML.metrics import ranking_report
class NCDM(PointNCDM):
def __init__(sel... | 4,592 | 37.275 | 108 | py |
EduCDM | EduCDM-main/EduCDM/IRR/etl/point_etl.py | # coding: utf-8
# 2021/6/19 @ tongshiwei
import os
import numpy as np
import pandas as pd
from longling import print_time
import torch
from torch.utils.data import TensorDataset, DataLoader
def extract(data_src, params):
with print_time("loading data from %s" % os.path.abspath(data_src), params.logger):
... | 1,113 | 26.170732 | 102 | py |
EduCDM | EduCDM-main/EduCDM/IRR/etl/pair_etl.py | # coding: utf-8
# 2021/6/19 @ tongshiwei
import torch
import os
from longling import print_time, iterwrap
import pandas as pd
import numpy as np
from longling.ML.toolkit.dataset import ItemSpecificSampler
__all__ = ["etl"]
def extract(data_src, params):
with print_time("loading data from %s" % os.path.abspath(d... | 2,058 | 32.754098 | 102 | py |
EduCDM | EduCDM-main/tests/mirt/conftest.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import random
import pytest
import torch
from torch.utils.data import TensorDataset, DataLoader
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
return user_num, item_num
@pytest.fixture(scope="package")
def data(conf):
user_num, item_n... | 783 | 21.4 | 54 | py |
EduCDM | EduCDM-main/tests/irt/gd/conftest.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import random
import pytest
import torch
from torch.utils.data import TensorDataset, DataLoader
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
return user_num, item_num
@pytest.fixture(scope="package")
def data(conf):
user_num, item_n... | 783 | 21.4 | 54 | py |
EduCDM | EduCDM-main/tests/kancd/conftest.py | # coding: utf-8
# 2023/3/8 @ WangFei
import random
import pytest
import torch
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
knowledge_num = 4
return user_num, item_num, knowledge_num
@pytest.fixture(s... | 1,122 | 25.738095 | 56 | py |
EduCDM | EduCDM-main/tests/dina/gd/conftest.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import random
import pytest
import torch
from torch.utils.data import TensorDataset, DataLoader
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
knowledge_num = 3
return user_num, item_num, knowledge_num
@pytest.fixture(scope="package")... | 993 | 24.487179 | 55 | py |
EduCDM | EduCDM-main/tests/ncdm/conftest.py | # coding: utf-8
# 2021/4/6 @ WangFei
import random
import pytest
import torch
import numpy as np
from torch.utils.data import TensorDataset, DataLoader
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
knowledge_num = 4
return user_num, item_num, knowledge_num
@pytest.fixture(s... | 1,122 | 25.738095 | 56 | py |
EduCDM | EduCDM-main/tests/mcd/conftest.py | # coding: utf-8
# 2021/3/23 @ tongshiwei
import random
import pytest
import torch
from torch.utils.data import TensorDataset, DataLoader
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
return user_num, item_num
@pytest.fixture(scope="package")
def data(conf):
user_num, item_n... | 783 | 21.4 | 54 | py |
CamStyle | CamStyle-master/main.py | from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import numpy as np
import sys
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader
from reid import datasets
from reid import models
from reid.trainers import Trainer, Ca... | 7,613 | 36.693069 | 122 | py |
CamStyle | CamStyle-master/reid/evaluators.py | from __future__ import print_function, absolute_import
import time
from collections import OrderedDict
import pdb
import torch
import numpy as np
from .evaluation_metrics import cmc, mean_ap
from .utils.meters import AverageMeter
from torch.autograd import Variable
from .utils import to_torch
from .utils import to_n... | 7,725 | 40.095745 | 120 | py |
CamStyle | CamStyle-master/reid/trainers.py | from __future__ import print_function, absolute_import
import time
import torch
from torch.autograd import Variable
from .evaluation_metrics import accuracy
from .loss import TripletLoss
from .utils.meters import AverageMeter
import pdb
class BaseTrainer(object):
def __init__(self, model, criterion):
su... | 6,168 | 34.65896 | 91 | py |
CamStyle | CamStyle-master/reid/models/resnet.py | from __future__ import absolute_import
from torch import nn
from torch.nn import functional as F
from torch.nn import init
import torchvision
import pdb
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
class ResNet(nn.Module):
__factory = {
18: torchvision.m... | 3,852 | 29.338583 | 80 | py |
CamStyle | CamStyle-master/reid/loss/lsr.py | from __future__ import absolute_import
import torch
from torch import nn
from torch.autograd import Variable
class LSRLoss(nn.Module):
def __init__(self, epsilon=0.1):
super(LSRLoss, self).__init__()
self.epsilon = epsilon
def forward(self, inputs, targets):
num_class = inputs.size()... | 957 | 30.933333 | 72 | py |
CamStyle | CamStyle-master/reid/loss/triplet.py | from __future__ import absolute_import
import torch
from torch import nn
from torch.autograd import Variable
class TripletLoss(nn.Module):
def __init__(self, margin=0):
super(TripletLoss, self).__init__()
self.margin = margin
self.ranking_loss = nn.MarginRankingLoss(margin=margin)
de... | 1,344 | 35.351351 | 73 | py |
CamStyle | CamStyle-master/reid/utils/__init__.py | from __future__ import absolute_import
import torch
def to_numpy(tensor):
if torch.is_tensor(tensor):
return tensor.cpu().numpy()
elif type(tensor).__module__ != 'numpy':
raise ValueError("Cannot convert {} to numpy array"
.format(type(tensor)))
return tensor
de... | 594 | 26.045455 | 60 | py |
CamStyle | CamStyle-master/reid/utils/serialization.py | from __future__ import print_function, absolute_import
import json
import os.path as osp
import shutil
import torch
from torch.nn import Parameter
from .osutils import mkdir_if_missing
def save_checkpoint(state, fpath='checkpoint.pth.tar'):
mkdir_if_missing(osp.dirname(fpath))
torch.save(state, fpath)
def... | 1,318 | 27.06383 | 74 | py |
CamStyle | CamStyle-master/reid/utils/data/sampler.py | from __future__ import absolute_import
from collections import defaultdict
import numpy as np
import torch
from torch.utils.data.sampler import (
Sampler, SequentialSampler, RandomSampler, SubsetRandomSampler,
WeightedRandomSampler)
class RandomIdentitySampler(Sampler):
def __init__(self, data_source, nu... | 1,191 | 32.111111 | 79 | py |
CamStyle | CamStyle-master/reid/utils/data/transforms.py | from __future__ import absolute_import
from torchvision.transforms import *
from PIL import Image
import random
import math
class RectScale(object):
def __init__(self, height, width, interpolation=Image.BILINEAR):
self.height = height
self.width = width
self.interpolation = interpolation
... | 2,565 | 30.679012 | 80 | py |
CamStyle | CamStyle-master/reid/evaluation_metrics/classification.py | from __future__ import absolute_import
from ..utils import to_torch
def accuracy(output, target, topk=(1,)):
output, target = to_torch(output), to_torch(target)
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.vi... | 521 | 25.1 | 73 | py |
CamStyle | CamStyle-master/CycleGAN-for-CamStyle/options/base_options.py | import argparse
import os
from util import util
import torch
import models
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, ... | 6,452 | 53.686441 | 192 | py |
CamStyle | CamStyle-master/CycleGAN-for-CamStyle/models/base_model.py | import os
import torch
from collections import OrderedDict
from . import networks
class BaseModel():
# modify parser to add command line options,
# and also change the default values if needed
@staticmethod
def modify_commandline_options(parser, is_train):
return parser
def name(self... | 6,011 | 38.038961 | 110 | py |
CamStyle | CamStyle-master/CycleGAN-for-CamStyle/models/pix2pix_model.py | import torch
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class Pix2PixModel(BaseModel):
def name(self):
return 'Pix2PixModel'
@staticmethod
def modify_commandline_options(parser, is_train=True):
parser.set_defaults(dataset_mode='aligned')... | 4,334 | 38.770642 | 134 | py |
CamStyle | CamStyle-master/CycleGAN-for-CamStyle/models/networks.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
###############################################################################
# Helper Functions
###############################################################################
def get_norm_layer(norm... | 15,716 | 40.036554 | 151 | py |
CamStyle | CamStyle-master/CycleGAN-for-CamStyle/models/cycle_gan_model.py | import torch
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class CycleGANModel(BaseModel):
def name(self):
return 'CycleGANModel'
@staticmethod
def modify_commandline_options(parser, is_train=True):
if is_train:
... | 7,083 | 45.300654 | 316 | py |
CamStyle | CamStyle-master/CycleGAN-for-CamStyle/util/image_pool.py | import random
import torch
class ImagePool():
def __init__(self, pool_size):
self.pool_size = pool_size
if self.pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, images):
if self.pool_size == 0:
return images
return_images = ... | 1,072 | 31.515152 | 93 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.