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How to use encode_result method in autotest
Best Python code snippet using autotest_python
read_data.py
Source:read_data.py 
1import numpy as np2import pandas as pd3import torch4from torch.utils.data import Dataset5from pytorch_transformers import *6import torch.utils.data as Data7import pickle8class Translator:9 """Backtranslation. Here to save time, we pre-processing and save all the translated data into pickle files.10 """11 def __init__(self, path, transform_type='BackTranslation'):12 # Pre-processed German data13 with open(path + 'de_1.pkl', 'rb') as f:14 self.de = pickle.load(f)15 # Pre-processed Russian data16 with open(path + 'ru_1.pkl', 'rb') as f:17 self.ru = pickle.load(f)18 def __call__(self, ori, idx):19 out1 = self.de[idx]20 out2 = self.ru[idx]21 return out1, out2, ori22def get_data(data_path, n_labeled_per_class, unlabeled_per_class=5000, max_seq_len=256, model='bert-base-uncased', train_aug=False):23 """Read data, split the dataset, and build dataset for dataloaders.24 Arguments:25 data_path {str} -- Path to your dataset folder: contain a train.csv and test.csv26 n_labeled_per_class {int} -- Number of labeled data per class27 Keyword Arguments:28 unlabeled_per_class {int} -- Number of unlabeled data per class (default: {5000})29 max_seq_len {int} -- Maximum sequence length (default: {256})30 model {str} -- Model name (default: {'bert-base-uncased'})31 train_aug {bool} -- Whether performing augmentation on labeled training set (default: {False})32 """33 # Load the tokenizer for bert34 tokenizer = BertTokenizer.from_pretrained(model)35 train_df = pd.read_csv(data_path+'train.csv', header=None)36 test_df = pd.read_csv(data_path+'test.csv', header=None)37 # Here we only use the bodies and removed titles to do the classifications38 train_labels = np.array([v-1 for v in train_df[0]])39 train_text = np.array([v for v in train_df[2]])40 test_labels = np.array([u-1 for u in test_df[0]])41 test_text = np.array([v for v in test_df[2]])42 n_labels = max(test_labels) + 143 # Split the labeled training set, unlabeled training set, development set44 train_labeled_idxs, train_unlabeled_idxs, val_idxs = train_val_split(45 train_labels, n_labeled_per_class, unlabeled_per_class, n_labels)46 # Build the dataset class for each set47 train_labeled_dataset = loader_labeled(48 train_text[train_labeled_idxs], train_labels[train_labeled_idxs], tokenizer, max_seq_len, train_aug)49 train_unlabeled_dataset = loader_unlabeled(50 train_text[train_unlabeled_idxs], train_unlabeled_idxs, tokenizer, max_seq_len, Translator(data_path))51 val_dataset = loader_labeled(52 train_text[val_idxs], train_labels[val_idxs], tokenizer, max_seq_len)53 test_dataset = loader_labeled(54 test_text, test_labels, tokenizer, max_seq_len)55 print("#Labeled: {}, Unlabeled {}, Val {}, Test {}".format(len(56 train_labeled_idxs), len(train_unlabeled_idxs), len(val_idxs), len(test_labels)))57 return train_labeled_dataset, train_unlabeled_dataset, val_dataset, test_dataset, n_labels58def train_val_split(labels, n_labeled_per_class, unlabeled_per_class, n_labels, seed=0):59 """Split the original training set into labeled training set, unlabeled training set, development set60 Arguments:61 labels {list} -- List of labeles for original training set62 n_labeled_per_class {int} -- Number of labeled data per class63 unlabeled_per_class {int} -- Number of unlabeled data per class64 n_labels {int} -- The number of classes65 Keyword Arguments:66 seed {int} -- [random seed of np.shuffle] (default: {0})67 Returns:68 [list] -- idx for labeled training set, unlabeled training set, development set69 """70 np.random.seed(seed)71 labels = np.array(labels)72 train_labeled_idxs = []73 train_unlabeled_idxs = []74 val_idxs = []75 for i in range(n_labels):76 idxs = np.where(labels == i)[0]77 np.random.shuffle(idxs)78 if n_labels == 2:79 # IMDB80 train_pool = np.concatenate((idxs[:500], idxs[5500:-2000]))81 train_labeled_idxs.extend(train_pool[:n_labeled_per_class])82 train_unlabeled_idxs.extend(83 idxs[500: 500 + 5000])84 val_idxs.extend(idxs[-2000:])85 elif n_labels == 10:86 # DBPedia87 train_pool = np.concatenate((idxs[:500], idxs[10500:-2000]))88 train_labeled_idxs.extend(train_pool[:n_labeled_per_class])89 train_unlabeled_idxs.extend(90 idxs[500: 500 + unlabeled_per_class])91 val_idxs.extend(idxs[-2000:])92 else:93 # Yahoo/AG News94 train_pool = np.concatenate((idxs[:500], idxs[5500:-2000]))95 train_labeled_idxs.extend(train_pool[:n_labeled_per_class])96 train_unlabeled_idxs.extend(97 idxs[500: 500 + 5000])98 val_idxs.extend(idxs[-2000:])99 np.random.shuffle(train_labeled_idxs)100 np.random.shuffle(train_unlabeled_idxs)101 np.random.shuffle(val_idxs)102 return train_labeled_idxs, train_unlabeled_idxs, val_idxs103class loader_labeled(Dataset):104 # Data loader for labeled data105 def __init__(self, dataset_text, dataset_label, tokenizer, max_seq_len, aug=False):106 self.tokenizer = tokenizer107 self.text = dataset_text108 self.labels = dataset_label109 self.max_seq_len = max_seq_len110 self.aug = aug111 self.trans_dist = {}112 if aug:113 print('Aug train data by back translation of German')114 self.en2de = torch.hub.load(115 'pytorch/fairseq', 'transformer.wmt19.en-de.single_model', tokenizer='moses', bpe='fastbpe')116 self.de2en = torch.hub.load(117 'pytorch/fairseq', 'transformer.wmt19.de-en.single_model', tokenizer='moses', bpe='fastbpe')118 def __len__(self):119 return len(self.labels)120 def augment(self, text):121 if text not in self.trans_dist:122 self.trans_dist[text] = self.de2en.translate(self.en2de.translate(123 text, sampling=True, temperature=0.9), sampling=True, temperature=0.9)124 return self.trans_dist[text]125 def get_tokenized(self, text):126 tokens = self.tokenizer.tokenize(text)127 if len(tokens) > self.max_seq_len:128 tokens = tokens[:self.max_seq_len]129 length = len(tokens)130 encode_result = self.tokenizer.convert_tokens_to_ids(tokens)131 padding = [0] * (self.max_seq_len - len(encode_result))132 encode_result += padding133 return encode_result, length134 def __getitem__(self, idx):135 if self.aug:136 text = self.text[idx]137 text_aug = self.augment(text)138 text_result, text_length = self.get_tokenized(text)139 text_result2, text_length2 = self.get_tokenized(text_aug)140 return ((torch.tensor(text_result), torch.tensor(text_result2)), (self.labels[idx], self.labels[idx]), (text_length, text_length2))141 else:142 text = self.text[idx]143 tokens = self.tokenizer.tokenize(text)144 if len(tokens) > self.max_seq_len:145 tokens = tokens[:self.max_seq_len]146 length = len(tokens)147 encode_result = self.tokenizer.convert_tokens_to_ids(tokens)148 padding = [0] * (self.max_seq_len - len(encode_result))149 encode_result += padding150 return (torch.tensor(encode_result), self.labels[idx], length)151class loader_unlabeled(Dataset):152 # Data loader for unlabeled data153 def __init__(self, dataset_text, unlabeled_idxs, tokenizer, max_seq_len, aug=None):154 self.tokenizer = tokenizer155 self.text = dataset_text156 self.ids = unlabeled_idxs157 self.aug = aug158 self.max_seq_len = max_seq_len159 def __len__(self):160 return len(self.text)161 def get_tokenized(self, text):162 tokens = self.tokenizer.tokenize(text)163 if len(tokens) > self.max_seq_len:164 tokens = tokens[:self.max_seq_len]165 length = len(tokens)166 encode_result = self.tokenizer.convert_tokens_to_ids(tokens)167 padding = [0] * (self.max_seq_len - len(encode_result))168 encode_result += padding169 return encode_result, length170 def __getitem__(self, idx):171 if self.aug is not None:172 u, v, ori = self.aug(self.text[idx], self.ids[idx])173 encode_result_u, length_u = self.get_tokenized(u)174 encode_result_v, length_v = self.get_tokenized(v)175 encode_result_ori, length_ori = self.get_tokenized(ori)176 return ((torch.tensor(encode_result_u), torch.tensor(encode_result_v), torch.tensor(encode_result_ori)), (length_u, length_v, length_ori))177 else:178 text = self.text[idx]179 encode_result, length = self.get_tokenized(text)...
test_encode.py
Source:test_encode.py
1from testplates import (2 struct,3 init,4 field,5 attach_codec,6 AnyInstance,7 encode,8 create_codec,9 TestplatesError,10 InvalidStructureError,11 NoCodecAvailableError,12 InaccessibleCodecError,13 AmbiguousCodecChoiceError,14)15from resultful import (16 success,17 failure,18 unwrap_success,19 unwrap_failure,20 Result,21)22from hypothesis import (23 given,24 strategies as st,25)26from .utils import (27 unreachable,28)29# noinspection PyTypeChecker30@given(data=st.binary())31def test_encode(data: bytes) -> None:32 metadata_object = object()33 # noinspection PyUnusedLocal34 def encode_function(35 metadata: object,36 instance: AnyInstance,37 ) -> Result[bytes, TestplatesError]:38 assert metadata is metadata_object39 return success(data)40 codec = create_codec(encode_function, unreachable)41 @struct42 class Person:43 pass44 attach_codec(Person, codec=codec, metadata=metadata_object)45 assert (person_result := init(Person))46 person = unwrap_success(person_result)47 assert (encode_result := encode(person))48 assert unwrap_success(encode_result) == data49# noinspection PyTypeChecker50@given(data=st.binary())51def test_encode_without_metadata(data: bytes) -> None:52 # noinspection PyUnusedLocal53 def encode_function(54 metadata: None,55 instance: AnyInstance,56 ) -> Result[bytes, TestplatesError]:57 assert metadata is None58 return success(data)59 codec = create_codec(encode_function, unreachable)60 @struct61 class Person:62 pass63 attach_codec(Person, codec=codec)64 assert (person_result := init(Person))65 person = unwrap_success(person_result)66 assert (encode_result := encode(person))67 assert unwrap_success(encode_result) == data68# noinspection PyTypeChecker69@given(data=st.binary())70def test_encode_with_using(data: bytes) -> None:71 # noinspection PyUnusedLocal72 def encode_function(73 metadata: None,74 instance: AnyInstance,75 ) -> Result[bytes, TestplatesError]:76 return success(data)77 primary = create_codec(encode_function, unreachable)78 secondary = create_codec(unreachable, unreachable)79 @struct80 class Person:81 pass82 attach_codec(Person, codec=primary)83 attach_codec(Person, codec=secondary)84 assert (person_result := init(Person))85 person = unwrap_success(person_result)86 assert (encode_result := encode(person, using=primary))87 assert unwrap_success(encode_result) is data88# noinspection PyTypeChecker89def test_encode_failure() -> None:90 error = TestplatesError()91 # noinspection PyUnusedLocal92 def encode_function(93 metadata: None,94 instance: AnyInstance,95 ) -> Result[bytes, TestplatesError]:96 return failure(error)97 codec = create_codec(encode_function, unreachable)98 @struct99 class Person:100 pass101 attach_codec(Person, codec=codec)102 assert (person_result := init(Person))103 person = unwrap_success(person_result)104 assert not (encode_result := encode(person))105 assert unwrap_failure(encode_result) is error106# noinspection PyTypeChecker107@given(name=st.text())108def test_encode_failure_invalid_structure(name: str) -> None:109 field_error = TestplatesError()110 codec = create_codec(unreachable, unreachable)111 @struct112 class Person:113 name = field(failure(field_error))114 attach_codec(Person, codec=codec)115 person = Person(name=name)116 assert not (encode_result := encode(person))117 error = unwrap_failure(encode_result)118 assert isinstance(error, InvalidStructureError)119 assert error.errors == [field_error]120# noinspection PyTypeChecker121def test_encode_failure_no_codec_available_error() -> None:122 @struct123 class Person:124 pass125 assert (person_result := init(Person))126 person = unwrap_success(person_result)127 assert not (encode_result := encode(person))128 error = unwrap_failure(encode_result)129 assert isinstance(error, NoCodecAvailableError)130 assert error.structure_type == Person131# noinspection PyTypeChecker132def test_encode_failure_inaccessible_codec_error() -> None:133 first = create_codec(unreachable, unreachable)134 second = create_codec(unreachable, unreachable)135 @struct136 class Person:137 pass138 attach_codec(Person, codec=first)139 assert (person_result := init(Person))140 person = unwrap_success(person_result)141 assert not (encode_result := encode(person, using=second))142 error = unwrap_failure(encode_result)143 assert isinstance(error, InaccessibleCodecError)144 assert error.structure_type == Person145 assert error.codecs == [first]146 assert error.using == second147# noinspection PyTypeChecker148def test_encode_failure_ambiguous_codec_choice_error() -> None:149 primary = create_codec(unreachable, unreachable)150 secondary = create_codec(unreachable, unreachable)151 @struct152 class Person:153 pass154 attach_codec(Person, codec=primary)155 attach_codec(Person, codec=secondary)156 assert (person_result := init(Person))157 person = unwrap_success(person_result)158 assert not (encode_result := encode(person))159 error = unwrap_failure(encode_result)160 assert isinstance(error, AmbiguousCodecChoiceError)161 assert error.structure_type == Person...
shumeitools.py
Source:shumeitools.py
1# -*- coding: utf-8 -*-2import json3import time4import urllib25PIC_CHANNEL = [6 (1,"IMAGE", "å¾åï¼é»è®¤å¼ï¼"),7 (2,"HEAD_IMG", "ç¨æ·å¤´å"),8 (3,"CHAT_IMG", "è天å¾ç"),9]10TEXT_CHANNEL = [11 (1, "NICKNAME"),12 (2, "MESSAGE"),13 (3, "PROFILE")14]15ACCESS_KEY = "IBIIH3VkFvqAEmGC3YpJ"16TEXT_TYPE = "SOCIAL"17PIC_TYPE = "AD_PORN"18def shumei_image_detect(pic_url, timeout,user_id,channel,sex,phone):19 channel_desc = "IMAGE"20 if channel==1:21 channel_desc = "IMAGE"22 elif channel == 2:23 channel_desc = "HEAD_IMGE"24 elif channel == 3:25 channel_desc = "CHAT_IMG"26 data = {"img": pic_url, "tokenId": str(user_id), "channel": channel_desc}27 playload = {"accessKey": ACCESS_KEY, "type": PIC_TYPE, "data": data, "sex":sex, "age":0, "phone":phone}28 body = json.dumps(playload)29 shumei_url = "http://api.fengkongcloud.com/v2/saas/anti_fraud/img"30 start_time = int(time.time()*1000)31 request = urllib2.Request(shumei_url, body)32 shumei_result = urllib2.urlopen(request, timeout=timeout).read()33 end_time = int(time.time()*1000)34 duration = end_time - start_time35 encode_result = json.loads(shumei_result)36 print encode_result37 if (encode_result["code"] == 1100):38 risk_level = encode_result["riskLevel"]39 detail = encode_result["detail"]40 elif (encode_result["code"] == 1902):41 # åæ°ä¸åæ³42 pass43 elif (encode_result["code"] == 1903):44 # æå¡å¤±è´¥45 pass46 elif (encode_result["code"] == 9100):47 # ä½é¢ä¸è¶³48 pass49 elif (encode_result["code"] == 9101):50 # æ æéæä½51 pass52 else:53 # ä¸æé误54 pass55 return encode_result, duration56def shumei_text_spam(text, timeout, user_id, channel, nickname,phone,ip):57 channel_desc = channel58 data = {"text": text, "tokenId": str(user_id), "channel": channel_desc, "nickname": nickname, "phone":phone, "ip":ip, }59 playload = {"accessKey": ACCESS_KEY, "type": TEXT_TYPE, "data": data}60 body = json.dumps(playload)61 shumei_url = "http://api.fengkongcloud.com/v2/saas/anti_fraud/text"62 request = urllib2.Request(shumei_url, body)63 start_time = int(time.time()*1000)64 shumei_result = urllib2.urlopen(request, timeout=timeout).read()65 end_time = int(time.time()*1000)66 duration = end_time - start_time67 encode_result = json.loads(shumei_result)68 #shumei_result = requests.post(shumei_url, data=body, timeout=timeout)69 #encode_result = json.loads(shumei_result.text)...
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