repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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DMASTE | DMASTE-main/BMRC/utils.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import torch
from torch.nn import functional as F
import logging
def normalize_size(tensor):
if len(tensor.size()) == 3:
tensor = tensor.contiguous().view(-1, tensor.size(2))
elif len(tensor.size()) == 2... | 3,575 | 35.121212 | 128 | py |
DMASTE | DMASTE-main/BMRC/data_utils.py | from torch.utils.data import Dataset
import random
import torch
class Domain:
Target = 1
Source = 0
class Unlabeled_Dataset(Dataset):
def __init__(self, path, tokenizer, max_len=256):
self.data = []
self.max_len = max_len
with open(path) as f:
for line in f:
... | 2,601 | 45.464286 | 150 | py |
DMASTE | DMASTE-main/BMRC/makeData_dual.py | # @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import torch
from torch.utils.data import Dataset
from transformers import BertTokenizer
import numpy as np
_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
class dual_sample(object):
def __init__(self,
... | 24,242 | 50.037895 | 144 | py |
DMASTE | DMASTE-main/BMRC/Model.py | # coding: UTF-8
# @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
from transformers import BertTokenizer, BertModel, BertConfig
import torch.nn as nn
class BERTModel(nn.Module):
def __init__(self, args):
hidden_size = args.hidden_size
super(BERTModel, self).__init... | 1,477 | 35.04878 | 104 | py |
DMASTE | DMASTE-main/BMRC/makeData_standard.py | # @Author: Shaowei Chen, Contact: chenshaowei0507@163.com
# @Date: 2021-5-4
import torch
import pickle
from dataProcess import get_text
def make_standard(home_path, dataset_name, dataset_type):
# read triple
f = open(home_path + dataset_name + "/" + dataset_type + ".txt", "r", encoding="utf-8")
te... | 2,219 | 35.393443 | 113 | py |
DMASTE | DMASTE-main/BMRC/scripts/cross-domain/dann/run.py | import os
import sys
import time
import random
import threading
source_list = ['electronics', 'home', 'beauty', 'fashion', 'all']
target_list = ['book', 'grocery', 'pet', 'toy']
class Param:
def __init__(self, model_name, source, target, ad_steps):
self.model_name = model_name
self.source = sou... | 1,911 | 31.965517 | 128 | py |
DMASTE | DMASTE-main/BMRC/scripts/cross-domain/dann/run_xu.py | import os
import sys
import time
import random
import threading
source_list = ['14res', '15res', '16res', '14lap', '14lap', '14lap']
target_list = ['14lap', '14lap', '14lap', '14res', '15res', '16res']
class Param:
def __init__(self, model_name, source, target, ad_steps):
self.model_name = model_name
... | 1,935 | 32.964912 | 128 | py |
DMASTE | DMASTE-main/Generative-ABSA/main.py | import argparse
import os
import logging
import time
import pickle
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning import seed_everything
from transformers import AdamW, T5ForConditionalGeneration, T5Tokenizer
from transformers import ge... | 16,407 | 42.638298 | 158 | py |
DMASTE | DMASTE-main/Generative-ABSA/convert_to_triplets.py | def idx2term(sent, triplets):
words = sent.split()
ret = []
for a, o, s in triplets:
a_term = words[a[0]: a[-1] + 1]
o_term = words[o[0]: o[-1] + 1]
ret.append((' '.join(a_term), ' '.join(o_term), s))
return ret
def convert(examples, all_preds, golden):
ret = []
for sent... | 1,537 | 39.473684 | 106 | py |
DMASTE | DMASTE-main/Generative-ABSA/data_utils.py | # This file contains all data loading and transformation functions
import time
from torch.utils.data import Dataset
senttag2word = {'POS': 'positive', 'NEG': 'negative', 'NEU': 'neutral'}
def read_line_examples_from_file(data_path):
"""
Read data from file, each line is: sent####labels
Return List[List[... | 11,367 | 34.304348 | 105 | py |
DMASTE | DMASTE-main/Generative-ABSA/error_analysis.py | import pickle as pk
def get_result(source, target):
sentences = f'data/aste/{target}/test.txt'
in_domain = f'log/results-aste-{target}.pickle'
cross_domain = f'log/results-aste-{source}_2_{target}.pickle'
lines = []
with open(sentences) as f:
for line in f:
lines.append(line.st... | 1,251 | 30.3 | 83 | py |
DMASTE | DMASTE-main/Generative-ABSA/eval_utils.py | # This file contains the evaluation functions
import re
import editdistance
sentiment_word_list = ['positive', 'negative', 'neutral']
aspect_cate_list = ['location general',
'food prices',
'food quality',
'ambience general',
'service general',
'restaurant prices',
'drinks prices',
'restaurant miscellaneous',
... | 11,361 | 31.462857 | 97 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/main.py | #coding utf-8
import json, os
import random
import argparse
import numpy
import torch
import torch.nn.functional as F
from tqdm import trange
import numpy as np
from data import load_data_instances, DataIterator
from model import MultiInferRNNModel, MultiInferCNNModel
import utils
def train(args):
# load doub... | 7,166 | 39.954286 | 127 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/attention_module.py | import copy
import math
import torch
import torch.nn.functional as F
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) \
/ math.sqrt(d_k)
if mask is not None:
... | 4,281 | 38.648148 | 112 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/utils.py | import multiprocessing
import pickle
import numpy as np
import sklearn
id2sentiment = {1: 'neg', 3: 'neu', 5: 'pos'}
def get_aspects(tags, length, ignore_index=-1):
spans = []
start = -1
for i in range(length):
if tags[i][i] == ignore_index: continue
elif tags[i][i] == 1:
if s... | 5,783 | 37.052632 | 105 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/model.py | import torch
import torch.nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
from attention_module import MultiHeadedAttention, SelfAttention
class MultiInferRNNModel(torch.nn.Module):
def __init__(self, gen_emb, domain_emb, args):
'''double embedd... | 7,886 | 41.632432 | 123 | py |
DMASTE | DMASTE-main/GTS/code/NNModel/data.py | import math
import torch
sentiment2id = {'negative': 3, 'neutral': 4, 'positive': 5}
def get_spans(tags):
'''for BIO tag'''
tags = tags.strip().split()
length = len(tags)
spans = []
start = -1
for i in range(length):
if tags[i].endswith('B'):
if start != -1:
... | 5,123 | 36.955556 | 93 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/main.py | #coding utf-8
import json, os
import random
import argparse
import torch
import torch.nn.functional as F
from tqdm import trange
from data import load_data_instances, DataIterator
from model import MultiInferBert
import utils
def train(args):
# load dataset
train_sentence_packs = json.load(open(args.prefi... | 6,176 | 37.60625 | 124 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/utils.py | import multiprocessing
import pickle
import numpy as np
import sklearn
id2sentiment = {1: 'neg', 3: 'neu', 5: 'pos'}
def get_aspects(tags, length, ignore_index=-1):
spans = []
start = -1
for i in range(length):
if tags[i][i] == ignore_index: continue
elif tags[i][i] == 1:
if s... | 8,201 | 43.096774 | 145 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/model.py | import torch
import torch.nn
from transformers import BertModel, BertTokenizer
class MultiInferBert(torch.nn.Module):
def __init__(self, args):
super(MultiInferBert, self).__init__()
self.args = args
self.bert = BertModel.from_pretrained(args.bert_model_path)
self.tokenizer = Ber... | 2,451 | 37.3125 | 114 | py |
DMASTE | DMASTE-main/GTS/code/BertModel/data.py | import math
import torch
import numpy as np
sentiment2id = {'negative': 3, 'neutral': 4, 'positive': 5}
from transformers import BertTokenizer
def get_spans(tags):
'''for BIO tag'''
tags = tags.strip().split()
length = len(tags)
spans = []
start = -1
for i in range(length):
if tags[i... | 7,269 | 36.864583 | 100 | py |
DMASTE | DMASTE-main/BARTABSA/peng/convert_to_triplets.py | from transformers import AutoTokenizer
import json
import numpy as np
def init_tokenizer():
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
unique_no_split_tokens = tokenizer.unique_no_split_tokens
tokenizer.unique_no_split_tokens = unique_no_split_tokens + ['[ia]']
tokenizer.add_toke... | 4,318 | 37.5625 | 92 | py |
DMASTE | DMASTE-main/BARTABSA/peng/__init__.py | 0 | 0 | 0 | py | |
DMASTE | DMASTE-main/BARTABSA/peng/train.py | import sys
sys.path.append('../')
import os
if 'p' in os.environ:
os.environ['CUDA_VISIBLE_DEVICES'] = os.environ['p']
# os.environ['CUDA_VISIBLE_DEVICES'] = '7'
import warnings
warnings.filterwarnings('ignore')
from data.pipe import BartBPEABSAPipe
from peng.model.bart_absa import BartSeq2SeqModel
from fastN... | 7,183 | 37.623656 | 128 | py |
DMASTE | DMASTE-main/BARTABSA/peng/data/pipe.py | from fastNLP.io import Pipe, DataBundle, Loader
import os
import json
from fastNLP import DataSet, Instance
from transformers import AutoTokenizer
import numpy as np
from itertools import chain
from functools import cmp_to_key
def cmp_aspect(v1, v2):
if v1[0]['from']==v2[0]['from']:
return v1[1]['from'] -... | 7,390 | 40.757062 | 148 | py |
DMASTE | DMASTE-main/BARTABSA/peng/data/__init__.py | 0 | 0 | 0 | py | |
DMASTE | DMASTE-main/BARTABSA/peng/model/losses.py |
from fastNLP import LossBase
import torch.nn.functional as F
from fastNLP import seq_len_to_mask
class Seq2SeqLoss(LossBase):
def __init__(self):
super().__init__()
def get_loss(self, tgt_tokens, tgt_seq_len, pred):
"""
:param tgt_tokens: bsz x max_len, [sos, tokens, eos]
:p... | 671 | 27 | 81 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/utils.py | import numpy as np
def get_max_len_max_len_a(data_bundle, max_len=10):
"""
:param data_bundle:
:param max_len:
:return:
"""
max_len_a = -1
for name, ds in data_bundle.iter_datasets():
if name=='train':continue
src_seq_len = np.array(ds.get_field('src_seq_len').content)
... | 756 | 26.035714 | 82 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/bart_absa.py | import torch
from .modeling_bart import BartEncoder, BartDecoder, BartModel
from transformers import BartTokenizer
from fastNLP import seq_len_to_mask
from fastNLP.modules import Seq2SeqEncoder, Seq2SeqDecoder, State
import torch.nn.functional as F
from fastNLP.models import Seq2SeqModel
from torch import nn
import mat... | 14,844 | 46.428115 | 145 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/modeling_bart.py | # coding=utf-8
# Copyright 2020 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LIC... | 58,282 | 41.234058 | 213 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/metrics.py | from fastNLP import MetricBase
from fastNLP.core.metrics import _compute_f_pre_rec
from collections import Counter
class Seq2SeqSpanMetric(MetricBase):
def __init__(self, eos_token_id, num_labels, opinion_first=True):
super(Seq2SeqSpanMetric, self).__init__()
self.eos_token_id = eos_token_id
... | 5,863 | 34.539394 | 120 | py |
DMASTE | DMASTE-main/BARTABSA/peng/model/__init__.py | 0 | 0 | 0 | py | |
DMASTE | DMASTE-main/BARTABSA/peng/model/generator.py | r"""Modify from fastNLP"""
import torch
from torch import nn
from fastNLP.models.seq2seq_model import Seq2SeqModel
from fastNLP.modules.decoder.seq2seq_decoder import Seq2SeqDecoder, State
import torch.nn.functional as F
from fastNLP.core.utils import _get_model_device
from functools import partial
class SequenceGen... | 23,989 | 44.435606 | 126 | py |
DMASTE | DMASTE-main/BARTABSA/data/process.py | import os
import json
def convert_triples(triples, words):
aspects = []
opinions = []
for i, triple in enumerate(triples):
a, o, s = triple
aspect = {'index': i, 'from': a[0], 'to': a[-1] + 1, 'polarity': s, 'term': words[a[0]: a[-1] + 1]}
opinion = {'index': i, 'from': o[0], 'to': ... | 1,503 | 32.422222 | 107 | py |
DMASTE | DMASTE-main/mySpanASTE/main.py | import os
import random
import argparse
import torch
from transformers import BertTokenizer, BertModel
from torch.utils.data import DataLoader
from torch.optim import AdamW
from tqdm import tqdm
from transformers.optimization import get_linear_schedule_with_warmup
from torch.utils.tensorboard import SummaryWriter
... | 8,336 | 46.369318 | 190 | py |
DMASTE | DMASTE-main/mySpanASTE/DANN_main.py | import os
import random
import argparse
import torch
from transformers import BertTokenizer, BertModel
from torch.utils.data import DataLoader
from torch.optim import AdamW
from tqdm import tqdm
from transformers.optimization import get_linear_schedule_with_warmup
from torch.utils.tensorboard import SummaryWriter
... | 10,335 | 47.754717 | 232 | py |
DMASTE | DMASTE-main/mySpanASTE/models/relation.py | from os import read
import torch
import math
from utils.data_utils import RelationLabel, SpanLabel
from utils.index_select import batched_index_select
from models.feedForward import FeedForward
def bucket_values(
distances: torch.Tensor, num_identity_buckets: int = 4, num_total_buckets: int = 10
) -> torch.Tens... | 8,024 | 51.796053 | 186 | py |
DMASTE | DMASTE-main/mySpanASTE/models/feedForward.py | import torch
class FeedForward(torch.nn.Module):
def __init__(self, input_dim, hidden_dim, num_layers, activation, dropout):
super(FeedForward, self).__init__()
hidden_dims = [hidden_dim] * num_layers # type: ignore
activations = [activation] * num_layers # type: ignore
dropout =... | 1,421 | 39.628571 | 79 | py |
DMASTE | DMASTE-main/mySpanASTE/models/DANN_span_aste.py | import torch
from torch.nn import functional as F
from utils.index_select import batched_index_select
from models.ner import NERModel
from models.relation import RelationModel
from models.functions import ReverseLayerF
class SpanModel(torch.nn.Module):
def __init__(self, encoder, width_embedding_dim=20, max_width... | 3,605 | 59.1 | 157 | py |
DMASTE | DMASTE-main/mySpanASTE/models/ner.py | import torch
from torch.nn.modules import dropout
import torch.nn.functional as F
from utils.data_utils import SpanLabel
from models.feedForward import FeedForward
class NERModel(torch.nn.Module):
def __init__(self, span_embed_dim, hidden_dim=150, num_layers=2, activation=torch.nn.ReLU(), dropout=0.4, n_labels=3... | 2,651 | 48.111111 | 188 | py |
DMASTE | DMASTE-main/mySpanASTE/models/functions.py | from torch.autograd import Function
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, alpha):
ctx.alpha = alpha
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
output = grad_output.neg() * ctx.alpha
return output, None | 305 | 18.125 | 46 | py |
DMASTE | DMASTE-main/mySpanASTE/models/span_aste.py | import torch
from utils.index_select import batched_index_select
from models.ner import NERModel
from models.relation import RelationModel
class SpanModel(torch.nn.Module):
def __init__(self, encoder, width_embedding_dim=20, max_width=512, spans_per_word=0.5):
super(SpanModel, self).__init__()
sel... | 2,370 | 52.886364 | 157 | py |
DMASTE | DMASTE-main/mySpanASTE/scripts/cross-domain/dann/run.py | import os
import sys
import time
import random
import threading
source_list = ['electronics', 'home', 'beauty', 'fashion', 'all']
target_list = ['book', 'grocery', 'pet', 'toy']
class Param:
def __init__(self, model_name, source, target, ad_steps):
self.model_name = model_name
self.source = sou... | 1,907 | 31.896552 | 128 | py |
DMASTE | DMASTE-main/mySpanASTE/scripts/cross-domain/dann/run_eq.py | import os
import sys
import time
import random
import threading
source_list = ['electronics', 'home', 'beauty', 'fashion', 'all']
target_list = ['book', 'grocery', 'pet', 'toy']
class Param:
def __init__(self, model_name, source, target, ad_steps):
self.model_name = model_name
self.source = sou... | 1,910 | 31.948276 | 131 | py |
DMASTE | DMASTE-main/mySpanASTE/scripts/cross-domain/dann/run_xu.py | import os
import sys
import time
import random
import threading
source_list = ['14res', '15res', '16res', '14lap', '14lap', '14lap']
target_list = ['14lap', '14lap', '14lap', '14res', '15res', '16res']
class Param:
def __init__(self, model_name, source, target, ad_steps):
self.model_name = model_name
... | 1,926 | 32.224138 | 128 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/data_utils_unlabeled.py | import os
from enum import IntEnum
from torch.utils.data import Dataset
class DomainLabel(IntEnum):
Source = 0
Target = 1
class UnlabeledDataset(Dataset):
def __init__(self, features):
self.features = features
def __getitem__(self, index):
return self.features[index]
... | 3,572 | 33.68932 | 92 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/collate_unlabeled.py | import torch
from utils.data_utils import RelationLabel
from utils.data_utils_unlabeled import DomainLabel
def collate_fn_target(data):
"""批处理，填充同一batch中句子最大的长度"""
def pad_and_tensor(data, pad_value=0):
max_len = max([len(x) for x in data])
new_data = []
mask = []
for x in dat... | 1,535 | 36.463415 | 99 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/data_utils.py | import os
from enum import IntEnum
from pydantic import BaseModel
from typing import List
from torch.utils.data import Dataset
import torch
class SpanLabel(IntEnum):
INVALID = 0
ASPECT = 1
OPINION = 2
class RelationLabel(IntEnum):
INVALID = 0
POS = 1
NEG = 2
NEU = 3
class ABSADataset... | 8,811 | 38.339286 | 155 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/collate.py | import torch
from utils.data_utils import RelationLabel
def collate_fn(data):
"""批处理，填充同一batch中句子最大的长度"""
def pad_and_tensor(data, pad_value=0):
max_len = max([len(x) for x in data])
new_data = []
mask = []
for x in data:
tmp_data = torch.tensor(x)
siz... | 2,502 | 39.370968 | 106 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/__init__.py | 0 | 0 | 0 | py | |
DMASTE | DMASTE-main/mySpanASTE/utils/index_select.py | import torch
def batched_index_select(target, indices):
"""
target : `torch.Tensor`, required.
A 3 dimensional tensor of shape (batch_size, sequence_length, embedding_size).
This is the tensor to be indexed.
indices : `torch.LongTensor`
A tensor of shape (batch_size, ...), where eac... | 1,622 | 42.864865 | 111 | py |
DMASTE | DMASTE-main/mySpanASTE/utils/metric.py | import torch
from utils.data_utils import convert_pad_tensor_to_list, convert_predictions_to_triples, SpanLabel, RelationLabel
from sklearn.metrics import precision_score, recall_score, f1_score
def convert_relations_to_list(relations, mask):
ret = []
for i in range(relations.shape[0]):
r, m = relatio... | 6,970 | 51.022388 | 159 | py |
GPOMCP | GPOMCP-master/logscan.py | import sys
import math
threshold_string = "threshold = "
reward_string = "Obtained an accum reward of: "
if len(sys.argv) != 4:
print "3 Arguments required: input_file and output_file and avg_out_file"
exit(0)
in_filename = sys.argv[1]
out_filename = sys.argv[2]
avg_out_filename = sys.argv[3]
in_file = open(... | 1,478 | 24.5 | 77 | py |
GPOMCP | GPOMCP-master/datscan.py | import sys
import math
threshold_string = "threshold = "
reward_string = "Obtained an accum reward of: "
if len(sys.argv) != 5:
print "4 Arguments required: input_file1 "\
"input_file2 and output_file and avg_out_file"
exit(0)
in_filename1 = sys.argv[1]
in_filename2 = sys.argv[2]
out_filename = sys.a... | 1,752 | 22.689189 | 69 | py |
GPOMCP | GPOMCP-master/Examples/Hallway/hallcp.py | from shutil import copyfile
import os
for i in range(1,10):
os.system("mkdir "+"./hallway"+str(i))
os.system("python genHallway.py "+"maph"+str(i)+".txt "+"hallway"+str(i)+".POMDP")
os.system("mv maph"+str(i)+ ".txt ./hallway"+str(i))
os.system("mv hallway"+str(i)+ ".POMDP ./hallway"+str(i))
| 298 | 32.222222 | 83 | py |
GPOMCP | GPOMCP-master/Examples/Hallway/genHallway.py | # Generates a POMDP file with a Hallway instance from a given maze map in a text file.
# Usage: python genHallway.py input_map.txt output_file [discount_factor]
# Hallway: a classic POMDP robot navigation benchmark. We have a maze with walls, traps and goals.
# The robot can move forward or turn left and right and it ... | 11,319 | 36.859532 | 147 | py |
GPOMCP | GPOMCP-master/Examples/Hallway/hallcp2.py | import os
for i in range(1,10):
os.system("python genHallway.py hallway"+str(i)+".txt hallway"+str(i)+".POMDP")
| 114 | 22 | 80 | py |
GPOMCP | GPOMCP-master/Examples/Hallway/MazeGen/MazeGeneratorRecursiveDivision.py | import random
import numpy
def isIn(i,j,num_rows,num_cols):
b = 1
if (i < 0) or (j < 0) or (i >= num_rows) or (j >= num_cols):
b=0;
return b;
def Modify(M,x_ul,y_ul,x_dr,y_dr):
if (x_dr - x_ul < 5) or (y_dr - y_ul < 5): pass
else:
x_cut = random.randint(x_ul+2,x_dr-2) if not (x_ul... | 2,613 | 25.673469 | 94 | py |
GPOMCP | GPOMCP-master/Examples/Hallway/MazeGen/MazeGenerator.py | import random
import numpy
def isIn(i,j,num_rows,num_cols):
b = 1
if (i < 0) or (j < 0) or (i >= num_rows) or (j >= num_cols):
b=0;
return b;
#input
num_rows = int(input("Rows-2: "))
num_cols = int(input("Columns-2: "))
num_nowall = int(input("Number of cells that are not walls: "))
num_trap = ... | 4,716 | 28.298137 | 118 | py |
GPOMCP | GPOMCP-master/Examples/rockSample/genScript/Coord.py | #dimX = 6
#dimY = 6
# dimY = 2
class Coord:
# (0,0) bottom left corner
def __init__(self,x,y,dim):
self.x = x
self.y = y
self.dim = dim
def setWalls(self,walls):
self.walls = walls
def __str__(self):
return "%dx%d" % (self.x, self.y)
def __repr__(self):
... | 2,694 | 24.186916 | 102 | py |
GPOMCP | GPOMCP-master/Examples/rockSample/genScript/State.py | from Coord import *
import itertools
class State:
def __init__(self, robot, mines, observation):
self.robot = robot
self.mines = mines
self.observation = observation
def __repr__(self):
return "N%sM%sT" % (str(self.robot),str(self.mines))
# return "(R:%s,T:%s,A: %s,O:%s)" % (self.robot,self.target,self.a... | 2,678 | 24.514286 | 158 | py |
GPOMCP | GPOMCP-master/Examples/rockSample/genScript/genRockSample.py | import itertools
import random
from State import *
from Coord import *
# Here you can set up rewards for various type of transitions and other
stepRew = -1
goodMineRew = 50
badMineRew = -25
illegalMoveRew = -100
discount = 0.98
sense = -5
assert not badMineRew == 0 and not goodMineRew == 0 and not badMineRew == g... | 15,375 | 31.033333 | 163 | py |
ges-idr5 | ges-idr5-master/cross_validate_sun.py |
"""
Cross-validate ensemble model on the sun.
"""
import yaml
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
from glob import glob
from code import (GESDatabase, plot, summary)
from astropy.table import Table
# Initialize logging.
logger = logging.getLogger("ges.idr5.qc")
logger.setLeve... | 2,635 | 22.327434 | 158 | py |
ges-idr5 | ges-idr5-master/setup.py | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import sys
from setuptools import setup, find_packages
from codecs import open
from os import path, system
from re import compile as re_compile
# For convenience.
if sys.argv[-1] == "publish":
system("python setup.py sdist upload")
sys.exit()
def read(filename):... | 1,640 | 28.836364 | 72 | py |
ges-idr5 | ges-idr5-master/sandbox_plot.py |
from code.model import ensemble, plot
raise a
for wg in (11, ):
for parameter in ("teff", "logg", "feh"):
model = ensemble.EnsembleModel.read(
"homogenisation-wg11-{}.model".format(parameter), None)
# Plot the distribution of biases for each node
fig = plot.biases(model... | 1,991 | 38.058824 | 86 | py |
ges-idr5 | ges-idr5-master/sandbox_plotting.py | #!/usr/bin/python
import yaml
import logging
import os
import matplotlib.pyplot as plt
from glob import glob
from code import (GESDatabase, plot, summary)
from astropy.table import Table
# Initialize logging.
logger = logging.getLogger("ges.idr5.qc")
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()... | 12,041 | 25.819599 | 89 | py |
ges-idr5 | ges-idr5-master/sandbox_ensemble.py |
"""
Sandbox for ensemble model
"""
import yaml
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
from glob import glob
from code import (GESDatabase, plot, summary)
from astropy.table import Table
# Initialize logging.
logger = logging.getLogger("ges.idr5.qc")
logger.setLevel(logging.DEBUG... | 7,903 | 27.534296 | 121 | py |
ges-idr5 | ges-idr5-master/sandbox_plot_wg_performance.py |
import yaml
from glob import glob
from code import (GESDatabase, plot, summary)
from astropy.table import Table
# Create a database object.
db_filename = "db.yaml"
with open(db_filename, "r") as fp:
credentials = yaml.load(fp)
database = GESDatabase(**credentials)
# Clean up bits and pieces...
savefig_kwds = ... | 3,134 | 30.35 | 95 | py |
ges-idr5 | ges-idr5-master/sandbox_plot_flags.py | #!/usr/bin/python
import yaml
from code import (GESDatabase, plot)
# Create a database object.
db_filename = "db.yaml"
with open(db_filename, "r") as fp:
credentials = yaml.load(fp)
database = GESDatabase(**credentials)
for wg in (10, 11, 12, 13):
fig, meta = plot.flags.heatmap(database, wg,
show_... | 751 | 24.931034 | 74 | py |
ges-idr5 | ges-idr5-master/sandbox_mean_ensemble.py |
"""
Sandbox for ensemble model
"""
import yaml
import logging
import os
import matplotlib.pyplot as plt
import numpy as np
from glob import glob
from code import (GESDatabase, plot, summary)
from astropy.table import Table
# Initialize logging.
logger = logging.getLogger("ges.idr5.qc")
logger.setLevel(logging.DEBUG... | 1,405 | 18.260274 | 114 | py |
ges-idr5 | ges-idr5-master/scripts/plot_korn_cluster.py |
import yaml
import logging
import os
import matplotlib.pyplot as plt
from glob import glob
from code import (GESDatabase, plot, summary)
from astropy.table import Table
# Initialize logging.
logger = logging.getLogger("ges.idr5.qc")
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
handler.setFormatt... | 2,899 | 25.363636 | 93 | py |
ges-idr5 | ges-idr5-master/scripts/propagate_flags.py | #!/usr/bin/python
"""
Propagate relevant flag information from one node to others.
"""
import logging
import yaml
from code import GESDatabase
# Connect to database.
db_filename = "db.yaml"
with open(db_filename, "r") as fp:
credentials = yaml.load(fp)
# Create a database object.
database = GESDatabase(**crede... | 10,591 | 35.524138 | 1,133 | py |
ges-idr5 | ges-idr5-master/scripts/homogenise_wg11.py |
"""
Homogenisation models.
"""
import yaml
import logging
import numpy as np
from code import GESDatabase
from code.model.ensemble import SingleParameterEnsembleModel
from astropy.table import Table
# Initialize logging.
logger = logging.getLogger("ges")
# Create a database object.
db_filename = "db.yaml"
with o... | 2,151 | 25.567901 | 95 | py |
ges-idr5 | ges-idr5-master/scripts/homogenise_fast.py |
"""
Homogenisation models.
"""
import yaml
import logging
import numpy as np
import os
from astropy.table import Table
from collections import OrderedDict
from code import GESDatabase
from code.model.ensemble import EnsembleModel, MedianModel
# Initialize logging.
logger = logging.getLogger("ges")
# Create a data... | 2,926 | 27.980198 | 107 | py |
ges-idr5 | ges-idr5-master/scripts/setup_db.py | #!/usr/bin/python
"""
Create database for the fifth internal data release of the Gaia-ESO Survey.
"""
import logging
import numpy as np
import psycopg2 as pg
import yaml
from glob import glob
# For fake data generation
import os
from astropy.io import fits
from code import GESDatabase
db_filename = "db.yaml"
nod... | 2,824 | 25.401869 | 82 | py |
ges-idr5 | ges-idr5-master/scripts/update_benchmarks.py |
"""
Update the benchmark parameters to include some values -- even if they are
uncertain -- and to include a less-biased value for HD 140283.
"""
from astropy.table import Table
input_path = "../fits-templates/benchmarks/GES_iDR5_FGKMCoolWarm_Benchmarks_AcceptedParams_01082016.fits"
output_path = "../fits-templates/... | 1,167 | 24.391304 | 105 | py |
ges-idr5 | ges-idr5-master/scripts/ship_wg11.py |
"""
Ship a WG11 recommended SP product.
"""
import yaml
import logging
from code import GESDatabase, ship
# Initialize logging.
logger = logging.getLogger("ges")
# Create a database object.
db_filename = "db.yaml"
with open(db_filename, "r") as fp:
credentials = yaml.load(fp)
database = GESDatabase(**credentia... | 845 | 25.4375 | 100 | py |
ges-idr5 | ges-idr5-master/scripts/homogenise_all_wgs.py |
"""
Homogenisation models.
"""
import yaml
import logging
import numpy as np
import os
from astropy.table import Table
from collections import OrderedDict
from code import GESDatabase
from code.model.ensemble import SingleParameterEnsembleModel
# Initialize logging.
logger = logging.getLogger("ges")
# Create a d... | 2,757 | 27.142857 | 95 | py |
ges-idr5 | ges-idr5-master/code/db.py |
""" A convenience object for databases. """
import logging
import numpy as np
import psycopg2 as pg
from astropy.table import Table
from collections import Counter
from decimal import Decimal
from time import time
logger = logging.getLogger("ges")
class Database(object):
def __init__(self, **kwargs):
... | 5,523 | 27.183673 | 81 | py |
ges-idr5 | ges-idr5-master/code/ship.py |
""" Fucking :shipit: """
import logging
import numpy as np
from astropy.io import fits
from astropy.table import Table
from datetime import datetime
import utils
from db import Database
logger = logging.getLogger("ges")
def wg_recommended_sp_template(database, input_path, output_path, wg,
ext=-1, overwrite=Fa... | 4,674 | 24.972222 | 78 | py |
ges-idr5 | ges-idr5-master/code/utils.py |
""" General utility functions. """
import os
from numpy import isfinite
def safe_int(x, fill_value=-1):
try:
return int(x)
except:
return fill_value
def wg_as_int(wg):
return int(str(wg).strip().lower().lstrip("wg"))
def parse_node_filename(filename):
# GES_iDR5_WG10_NodeTemplate... | 675 | 17.777778 | 52 | py |
ges-idr5 | ges-idr5-master/code/__init__.py |
import logging
logger = logging.getLogger("ges")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter(
"%(asctime)s [%(levelname)-8s] %(message)s"))
logger.addHandler(handler)
from . import model
from .gesdb import GESDatabase
| 285 | 19.428571 | 49 | py |
ges-idr5 | ges-idr5-master/code/gesdb.py |
""" A specialized database class for Gaia-ESO Survey data releases. """
import logging
import numpy as np
from astropy.io import fits
from astropy.table import Table
import utils
from db import Database
logger = logging.getLogger("ges")
class GESDatabase(Database):
def __init__(self, *args, **kwargs):
... | 10,196 | 28.903226 | 99 | py |
ges-idr5 | ges-idr5-master/code/summary.py |
""" Produce summary tables. """
import numpy as np
from collections import Counter
from astropy.table import Table
import utils
def stellar_parameter_range(database, wg=None):
"""
Produce a summary table outlining the range of stellar parameters reported.
:param database:
A database for transa... | 4,274 | 28.895105 | 86 | py |
ges-idr5 | ges-idr5-master/code/plot/nodes.py |
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib import gridspec
import corner
import utils
__all__ = ["compare_nodes_within_wg", "compare_to_photometric_teff",
"compare_to_previous_dr"]
def compare_to_previous_dr(database, wg, node_name,... | 9,929 | 30.324921 | 80 | py |
ges-idr5 | ges-idr5-master/code/plot/cluster.py |
import logging
import numpy as np
import os
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib import gridspec
import utils
__all__ = ["cluster", "param_vs_param"]
logger = logging.getLogger("ges.idr5.qc")
def param_vs_param(database, wg, node_name, ges_fld, reference_parame... | 9,020 | 30.876325 | 84 | py |
ges-idr5 | ges-idr5-master/code/plot/benchmarks.py |
import logging
import numpy as np
import os
import matplotlib.pyplot as plt
from astropy.table import Table
from matplotlib.ticker import MaxNLocator
from matplotlib import gridspec
import utils
__all__ = ["node_benchmark_performance", "wg_benchmark_performance"]
benchmark_filename = "fits-templates/benchmarks/GES... | 13,779 | 34.792208 | 110 | py |
ges-idr5 | ges-idr5-master/code/plot/utils.py |
import numpy as np
from astropy.table import Table
def parse_isochrone(filename):
"""
Parse a PARSEC or Siess isochrone.
:param filename:
The filename of the isochrone.
"""
is_parsec = "parsec" in filename.lower()
kwds = {
"format": "ascii"
}
if is_parsec:
kwd... | 1,119 | 19.363636 | 78 | py |
ges-idr5 | ges-idr5-master/code/plot/flags.py |
import numpy as np
import matplotlib.pyplot as plt
from itertools import combinations
from matplotlib.colors import LogNorm
import scipy.sparse.csgraph
_WG14_NODE_IDS = {
"01": "Arcetri",
"02": "CAUP",
"03": "EPINARBO",
"04": "IAC",
"05": "Lumba",
"06": "MaxPlanck",
"07": "MyGIsFOS",
... | 9,136 | 30.725694 | 87 | py |
ges-idr5 | ges-idr5-master/code/plot/__init__.py | from hrd import *
from cluster import *
from nodes import *
from benchmarks import *
import flags | 97 | 18.6 | 24 | py |
ges-idr5 | ges-idr5-master/code/plot/hrd.py |
__all__ = ["hrd", "stellar_parameter_histograms", "stellar_parameter_error_histograms",
"hrd_by_setup"]
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib import gridspec
import utils
def hrd_by_setup(database, wg, node_name):
"""
Show Hertszpr... | 9,544 | 28.642857 | 87 | py |
ges-idr5 | ges-idr5-master/code/model/plot.py |
"""
Plot things relevant to the ensemble model.
"""
import cPickle as pickle
import itertools
import logging
import numpy as np
import scipy.sparse
import matplotlib.pyplot as plt
from collections import OrderedDict
from matplotlib.ticker import MaxNLocator
import brewer2mpl # For pretty colors.
logger = logging.g... | 22,417 | 30.619182 | 124 | py |
ges-idr5 | ges-idr5-master/code/model/__init__.py | from .ensemble import * | 23 | 23 | 23 | py |
ges-idr5 | ges-idr5-master/code/model/ensemble.py |
"""
Classes to deal with homogenisation models written in Stan.
"""
import cPickle as pickle
import logging
import numpy as np
import os
import pystan as stan
from astropy.table import Table
from collections import OrderedDict
from itertools import combinations
from time import time
from . import plot
logger = logg... | 40,550 | 34.854111 | 98 | py |
YouTokenToMe | YouTokenToMe-master/setup.py | import io
import os
from setuptools import Extension, find_packages, setup
from Cython.Build import cythonize
extensions = [
Extension(
"_youtokentome_cython",
[
"youtokentome/cpp/yttm.pyx",
"youtokentome/cpp/bpe.cpp",
"youtokentome/cpp/utils.cpp",
"... | 1,673 | 29.436364 | 82 | py |
YouTokenToMe | YouTokenToMe-master/tests/speed_test/speed_test.py | import argparse
import os
from pathlib import Path
from time import time
from tabulate import tabulate
from tokenizers import pre_tokenizers
from tokenizers import Tokenizer as HuggingFaceBPETokenizer
from tokenizers.models import BPE as HuggingFaceBPEModel
from tokenizers.trainers import BpeTrainer as HuggingFaceBPET... | 9,224 | 34.755814 | 120 | py |
YouTokenToMe | YouTokenToMe-master/tests/unit_tests/test_cli.py | import os
import random
from subprocess import run
from utils_for_testing import (
BASE_MODEL_FILE,
RENAME_ID_MODEL_FILE,
TEST_FILE,
TRAIN_FILE,
BOS_ID,
EOS_ID,
file_starts_with,
generate_artifacts,
)
def test_bos_eos_reverse():
generate_artifacts()
cmd_args = [
"yttm"... | 5,301 | 23.892019 | 114 | py |
YouTokenToMe | YouTokenToMe-master/tests/unit_tests/test_stress.py | import os
from subprocess import run
tests_compiled = False
def compile_test():
global tests_compiled
if tests_compiled:
return
build_files = ["bpe.cpp", "utils.cpp", "utf8.cpp"]
files = ["../../youtokentome/cpp/" + file_name for file_name in build_files]
files.append("stress_test.cpp")
... | 1,115 | 20.882353 | 90 | py |
YouTokenToMe | YouTokenToMe-master/tests/unit_tests/test_python_api.py | import os
import random
import youtokentome as yttm
from utils_for_testing import (
BASE_MODEL_FILE,
RENAME_ID_MODEL_FILE,
TEST_FILE,
TRAIN_FILE,
BOS_ID,
EOS_ID,
file_starts_with,
generate_artifacts,
)
def test_encode_decode():
generate_artifacts()
os.remove(BASE_MODEL_FILE)
... | 1,411 | 26.153846 | 86 | py |
YouTokenToMe | YouTokenToMe-master/tests/unit_tests/test_manual.py | # -*- coding: utf-8 -*-
import os
import youtokentome as yttm
def test_russian():
train_text = """
собирать cборник сборище отобранный сборщица
"""
test_text = """
собранный собрание прибор
"""
TRAIN_DATA_PATH = "train_data.txt"
MODEL_PATH = "model.yttm"
with op... | 2,294 | 29.197368 | 113 | py |
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