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How to use data_base_path method in play_selenium
Best Python code snippet using play_selenium_python
data_io.py
Source:data_io.py 
1import asyncio2import os3from collections import Counter, OrderedDict4from multiprocessing import Pool5import numpy as np6import torch7import torch.nn as nn8import torch.nn.functional as F9from kaldi_io import read_mat, read_vec_flt10from sklearn.preprocessing import KBinsDiscretizer, LabelEncoder, StandardScaler11from torch.utils.data import Dataset12class MissingClassMapError(Exception):13 pass14def load_n_col(file, numpy=False):15 data = []16 with open(file) as fp:17 for line in fp:18 data.append(line.strip().split(" "))19 columns = list(zip(*data))20 if numpy:21 columns = [np.array(list(i)) for i in columns]22 else:23 columns = [list(i) for i in columns]24 return columns25def odict_from_2_col(file, numpy=False):26 col0, col1 = load_n_col(file, numpy=numpy)27 return OrderedDict({c0: c1 for c0, c1 in zip(col0, col1)})28def load_one_tomany(file, numpy=False):29 one = []30 many = []31 with open(file) as fp:32 for line in fp:33 line = line.strip().split(" ", 1)34 one.append(line[0])35 m = line[1].split(" ")36 many.append(np.array(m) if numpy else m)37 if numpy:38 one = np.array(one)39 return one, many40def train_transform(feats, seqlen):41 leeway = feats.shape[0] - seqlen42 startslice = np.random.randint(0, int(leeway)) if leeway > 0 else 043 feats = (44 feats[startslice : startslice + seqlen]45 if leeway > 046 else np.pad(feats, [(0, -leeway), (0, 0)], "constant")47 )48 return torch.FloatTensor(feats)49async def get_item_train(instructions):50 fpath = instructions[0]51 seqlen = instructions[1]52 raw_feats = read_mat(fpath)53 feats = train_transform(raw_feats, seqlen)54 return feats55async def get_item_test(filepath):56 raw_feats = read_mat(filepath)57 return torch.FloatTensor(raw_feats)58def async_map(coroutine_func, iterable):59 loop = asyncio.get_event_loop()60 future = asyncio.gather(*(coroutine_func(param) for param in iterable))61 return loop.run_until_complete(future)62class SpeakerDataset(Dataset):63 def __init__(64 self,65 data_base_path,66 real_speaker_labels=True,67 asynchr=True,68 num_workers=3,69 test_mode=False,70 class_enc_dict={},71 **kwargs72 ):73 self.data_base_path = data_base_path74 self.num_workers = num_workers75 self.test_mode = test_mode76 self.real_speaker_labels = real_speaker_labels77 # self.label_types = label_types78 if self.test_mode:79 self.label_types = []80 else:81 self.label_types = ["speaker"] if self.real_speaker_labels else []82 if os.path.isfile(os.path.join(data_base_path, "spk2nat")):83 self.label_types.append("nationality")84 if os.path.isfile(os.path.join(data_base_path, "spk2gender")):85 self.label_types.append("gender")86 if os.path.isfile(os.path.join(data_base_path, "utt2age")):87 self.label_types.append("age_regression")88 self.label_types.append("age")89 if os.path.isfile(os.path.join(data_base_path, "utt2rec")):90 self.label_types.append("rec")91 if os.path.isfile(os.path.join(data_base_path, "utt2genre")):92 self.label_types.append("genre")93 if self.test_mode and self.label_types:94 assert class_enc_dict, "Class mapping must be passed to test mode dataset"95 self.class_enc_dict = class_enc_dict96 utt2spk_path = os.path.join(data_base_path, "utt2spk")97 spk2utt_path = os.path.join(data_base_path, "spk2utt")98 feats_scp_path = os.path.join(data_base_path, "feats.scp")99 assert os.path.isfile(utt2spk_path)100 assert os.path.isfile(feats_scp_path)101 assert os.path.isfile(spk2utt_path)102 verilist_path = os.path.join(data_base_path, "veri_pairs")103 if self.test_mode:104 if os.path.isfile(verilist_path):105 self.veri_labs, self.veri_0, self.veri_1 = load_n_col(106 verilist_path, numpy=True107 )108 self.veri_labs = self.veri_labs.astype(int)109 self.veripairs = True110 else:111 self.veripairs = False112 self.utts, self.uspkrs = load_n_col(utt2spk_path)113 self.utt_fpath_dict = odict_from_2_col(feats_scp_path)114 self.label_enc = LabelEncoder()115 self.original_spkrs, self.spkutts = load_one_tomany(spk2utt_path)116 self.spkrs = self.label_enc.fit_transform(self.original_spkrs)117 self.spk_utt_dict = OrderedDict(118 {k: v for k, v in zip(self.spkrs, self.spkutts)}119 )120 self.spk_original_spk_dict = {121 k: v for k, v in zip(self.spkrs, self.original_spkrs)122 }123 self.uspkrs = self.label_enc.transform(self.uspkrs)124 self.utt_spkr_dict = OrderedDict({k: v for k, v in zip(self.utts, self.uspkrs)})125 self.utt_list = list(self.utt_fpath_dict.keys())126 self.first_batch = True127 self.num_classes = (128 {"speaker": len(self.label_enc.classes_)}129 if self.real_speaker_labels130 else {}131 )132 self.asynchr = asynchr133 if "nationality" in self.label_types:134 self.natspkrs, self.nats = load_n_col(135 os.path.join(data_base_path, "spk2nat")136 )137 self.nats = [n.lower().strip() for n in self.nats]138 self.natspkrs = self.label_enc.transform(self.natspkrs)139 self.nat_label_enc = LabelEncoder()140 if not self.test_mode:141 self.nats = self.nat_label_enc.fit_transform(self.nats)142 else:143 self.nat_label_enc = self.class_enc_dict["nationality"]144 self.nats = self.nat_label_enc.transform(self.nats)145 self.spk_nat_dict = OrderedDict(146 {k: v for k, v in zip(self.natspkrs, self.nats)}147 )148 self.num_classes["nationality"] = len(self.nat_label_enc.classes_)149 if "gender" in self.label_types:150 self.genspkrs, self.genders = load_n_col(151 os.path.join(data_base_path, "spk2gender")152 )153 self.genspkrs = self.label_enc.transform(self.genspkrs)154 self.gen_label_enc = LabelEncoder()155 if not self.test_mode:156 self.genders = self.gen_label_enc.fit_transform(self.genders)157 else:158 self.gen_label_enc = self.class_enc_dict["gender"]159 self.genders = self.gen_label_enc.transform(self.genders)160 self.spk_gen_dict = OrderedDict(161 {k: v for k, v in zip(self.genspkrs, self.genders)}162 )163 self.num_classes["gender"] = len(self.gen_label_enc.classes_)164 if "age" in self.label_types:165 # self.genspkrs, self.genders = load_n_col(os.path.join(data_base_path, 'spk2gender'))166 self.num_age_bins = (167 kwargs["num_age_bins"] if "num_age_bins" in kwargs else 10168 )169 self.ageutts, self.ages = load_n_col(170 os.path.join(data_base_path, "utt2age")171 )172 self.ages = np.array(self.ages).astype(np.float)173 self.age_label_enc = KBinsDiscretizer(174 n_bins=self.num_age_bins, encode="ordinal", strategy="uniform"175 )176 if not self.test_mode or "age" not in self.class_enc_dict:177 self.age_classes = self.age_label_enc.fit_transform(178 np.array(self.ages).reshape(-1, 1)179 ).flatten()180 else:181 self.age_label_enc = self.class_enc_dict["age"]182 self.age_classes = self.age_label_enc.transform(183 np.array(self.ages).reshape(-1, 1)184 ).flatten()185 self.utt_age_class_dict = OrderedDict(186 {k: v for k, v in zip(self.ageutts, self.age_classes)}187 )188 self.num_classes["age"] = self.num_age_bins189 if "age_regression" in self.label_types:190 # self.genspkrs, self.genders = load_n_col(os.path.join(data_base_path, 'spk2gender'))191 self.ageutts, self.ages = load_n_col(192 os.path.join(data_base_path, "utt2age")193 )194 self.ages = np.array(self.ages).astype(np.float)195 self.age_reg_enc = StandardScaler()196 if not self.test_mode or "age_regression" not in self.class_enc_dict:197 self.ages = self.age_reg_enc.fit_transform(198 np.array(self.ages).reshape(-1, 1)199 ).flatten()200 else:201 self.age_reg_enc = self.class_enc_dict["age_regression"]202 self.ages = self.age_reg_enc.transform(203 np.array(self.ages).reshape(-1, 1)204 ).flatten()205 self.utt_age_dict = OrderedDict(206 {k: v for k, v in zip(self.ageutts, self.ages)}207 )208 self.num_classes["age_regression"] = 1209 if "rec" in self.label_types:210 self.recutts, self.recs = load_n_col(211 os.path.join(data_base_path, "utt2rec")212 )213 self.recs = np.array(self.recs)214 self.rec_label_enc = LabelEncoder()215 if not self.test_mode:216 self.recs = self.rec_label_enc.fit_transform(self.recs)217 else:218 self.rec_label_enc = self.class_enc_dict["rec"]219 self.recs = self.rec_label_enc.transform(self.recs)220 self.utt_rec_dict = OrderedDict(221 {k: v for k, v in zip(self.recutts, self.recs)}222 )223 self.num_classes["rec"] = len(self.rec_label_enc.classes_)224 if "genre" in self.label_types:225 self.genreutts, self.genres = load_n_col(226 os.path.join(data_base_path, "utt2genre")227 )228 self.genres = np.array(self.genres)229 self.genre_label_enc = LabelEncoder()230 if not self.test_mode:231 self.genres = self.genre_label_enc.fit_transform(self.genres)232 self.utt_genre_dict = OrderedDict(233 {k: v for k, v in zip(self.genreutts, self.genres)}234 )235 self.num_classes["genre"] = len(self.genre_label_enc.classes_)236 else:237 # TODO: add this check to other attributes238 if "genre" in self.class_enc_dict:239 self.genre_label_enc = self.class_enc_dict["genre"]240 self.genres = self.genre_label_enc.transform(self.genres)241 self.utt_genre_dict = OrderedDict(242 {k: v for k, v in zip(self.genreutts, self.genres)}243 )244 self.num_classes["genre"] = len(self.genre_label_enc.classes_)245 else:246 self.label_types.remove("genre")247 self.class_enc_dict = self.get_class_encs()248 def __len__(self):249 return len(self.utt_list)250 def get_class_encs(self):251 class_enc_dict = {}252 if "speaker" in self.label_types:253 class_enc_dict["speaker"] = self.label_enc254 if "age" in self.label_types:255 class_enc_dict["age"] = self.age_label_enc256 if "age_regression" in self.label_types:257 class_enc_dict["age_regression"] = self.age_reg_enc258 if "nationality" in self.label_types:259 class_enc_dict["nationality"] = self.nat_label_enc260 if "gender" in self.label_types:261 class_enc_dict["gender"] = self.gen_label_enc262 if "rec" in self.label_types:263 class_enc_dict["rec"] = self.rec_label_enc264 if "genre" in self.label_types:265 class_enc_dict["genre"] = self.genre_label_enc266 self.class_enc_dict = class_enc_dict267 return class_enc_dict268 @staticmethod269 def get_item(instructions):270 fpath = instructions[0]271 seqlen = instructions[1]272 feats = read_mat(fpath)273 feats = train_transform(feats, seqlen)274 return feats275 def get_item_test(self, idx):276 utt = self.utt_list[idx]277 fpath = self.utt_fpath_dict[utt]278 feats = read_mat(fpath)279 feats = torch.FloatTensor(feats)280 label_dict = {}281 speaker = self.utt_spkr_dict[utt]282 if "speaker" in self.label_types:283 label_dict["speaker"] = torch.LongTensor([speaker])284 if "gender" in self.label_types:285 label_dict["gender"] = torch.LongTensor([self.spk_gen_dict[speaker]])286 if "nationality" in self.label_types:287 label_dict["nationality"] = torch.LongTensor([self.spk_nat_dict[speaker]])288 if "age" in self.label_types:289 label_dict["age"] = torch.LongTensor([self.utt_age_class_dict[utt]])290 if "age_regression" in self.label_types:291 label_dict["age_regression"] = torch.FloatTensor([self.utt_age_dict[utt]])292 if "genre" in self.label_types:293 label_dict["genre"] = torch.LongTensor([self.utt_genre_dict[utt]])294 return feats, label_dict295 def get_test_items(self, num_items=-1, exclude_speakers=None, use_async=True):296 utts = self.utt_list297 if num_items >= 1:298 replace = len(utts) <= num_items299 utts = np.random.choice(utts, size=num_items, replace=replace)300 utts = np.array(utts)301 spkrs = np.array([self.utt_spkr_dict[utt] for utt in utts])302 original_spkrs = np.array([self.spk_original_spk_dict[spkr] for spkr in spkrs])303 if exclude_speakers:304 mask = np.array(305 [False if s in exclude_speakers else True for s in original_spkrs]306 )307 utts = utts[mask]308 spkrs = spkrs[mask]309 original_spkrs = original_spkrs[mask]310 fpaths = [self.utt_fpath_dict[utt] for utt in utts]311 if use_async:312 feats = async_map(get_item_test, fpaths)313 else:314 feats = [torch.FloatTensor(read_mat(f)) for f in fpaths]315 label_dict = {}316 label_dict["speaker"] = np.array(spkrs)317 label_dict["original_speaker"] = np.array(original_spkrs)318 if "nationality" in self.label_types:319 label_dict["nationality"] = np.array([self.spk_nat_dict[s] for s in spkrs])320 if "gender" in self.label_types:321 label_dict["gender"] = np.array([self.spk_gen_dict[s] for s in spkrs])322 if "age" in self.label_types:323 label_dict["age"] = np.array([self.utt_age_class_dict[utt] for utt in utts])324 if "age_regression" in self.label_types:325 label_dict["age_regression"] = np.array(326 [self.utt_age_dict[utt] for utt in utts]327 )328 if "genre" in self.label_types:329 label_dict["genre"] = np.array([self.utt_genre_dict[utt] for utt in utts])330 return feats, label_dict, utts331 def get_batches(self, batch_size=256, max_seq_len=400, sp_tensor=True):332 """333 Main data iterator, specify batch_size and max_seq_len334 sp_tensor determines whether speaker labels are returned as Tensor object or not335 """336 # with Parallel(n_jobs=self.num_workers) as parallel:337 self.idpool = self.spkrs.copy()338 assert batch_size < len(339 self.idpool340 ) # Metric learning assumption large num classes341 lens = [max_seq_len for _ in range(batch_size)]342 while True:343 if len(self.idpool) <= batch_size:344 batch_ids = np.array(self.idpool)345 self.idpool = self.spkrs.copy()346 rem_ids = np.random.choice(347 self.idpool, size=batch_size - len(batch_ids), replace=False348 )349 batch_ids = np.concatenate([batch_ids, rem_ids])350 self.idpool = list(set(self.idpool) - set(rem_ids))351 else:352 batch_ids = np.random.choice(353 self.idpool, size=batch_size, replace=False354 )355 self.idpool = list(set(self.idpool) - set(batch_ids))356 batch_fpaths = []357 batch_utts = []358 for i in batch_ids:359 utt = np.random.choice(self.spk_utt_dict[i])360 batch_utts.append(utt)361 batch_fpaths.append(self.utt_fpath_dict[utt])362 if self.asynchr:363 batch_feats = async_map(get_item_train, zip(batch_fpaths, lens))364 else:365 batch_feats = [self.get_item(a) for a in zip(batch_fpaths, lens)]366 # batch_feats = parallel(delayed(self.get_item)(a) for a in zip(batch_fpaths, lens))367 label_dict = {}368 if "speaker" in self.label_types:369 label_dict["speaker"] = (370 torch.LongTensor(batch_ids) if sp_tensor else batch_ids371 )372 if "nationality" in self.label_types:373 label_dict["nationality"] = torch.LongTensor(374 [self.spk_nat_dict[s] for s in batch_ids]375 )376 if "gender" in self.label_types:377 label_dict["gender"] = torch.LongTensor(378 [self.spk_gen_dict[s] for s in batch_ids]379 )380 if "age" in self.label_types:381 label_dict["age"] = torch.LongTensor(382 [self.utt_age_class_dict[u] for u in batch_utts]383 )384 if "age_regression" in self.label_types:385 label_dict["age_regression"] = torch.FloatTensor(386 [self.utt_age_dict[u] for u in batch_utts]387 )388 if "rec" in self.label_types:389 label_dict["rec"] = torch.LongTensor(390 [self.utt_rec_dict[u] for u in batch_utts]391 )392 if "genre" in self.label_types:393 label_dict["genre"] = torch.LongTensor(394 [self.utt_genre_dict[u] for u in batch_utts]395 )396 yield torch.stack(batch_feats), label_dict397 def get_batches_naive(self, batch_size=256, max_seq_len=400, sp_tensor=True):398 """399 Main data iterator, specify batch_size and max_seq_len400 sp_tensor determines whether speaker labels are returned as Tensor object or not401 """402 self.idpool = self.spkrs.copy()403 # assert batch_size < len(self.idpool) #Metric learning assumption large num classes404 lens = [max_seq_len for _ in range(batch_size)]405 while True:406 batch_ids = np.random.choice(self.idpool, size=batch_size)407 batch_fpaths = []408 batch_utts = []409 for i in batch_ids:410 utt = np.random.choice(self.spk_utt_dict[i])411 batch_utts.append(utt)412 batch_fpaths.append(self.utt_fpath_dict[utt])413 if self.asynchr:414 batch_feats = async_map(get_item_train, zip(batch_fpaths, lens))415 else:416 batch_feats = [self.get_item(a) for a in zip(batch_fpaths, lens)]417 # batch_feats = parallel(delayed(self.get_item)(a) for a in zip(batch_fpaths, lens))418 label_dict = {}419 if "speaker" in self.label_types:420 label_dict["speaker"] = (421 torch.LongTensor(batch_ids) if sp_tensor else batch_ids422 )423 if "nationality" in self.label_types:424 label_dict["nationality"] = torch.LongTensor(425 [self.spk_nat_dict[s] for s in batch_ids]426 )427 if "gender" in self.label_types:428 label_dict["gender"] = torch.LongTensor(429 [self.spk_gen_dict[s] for s in batch_ids]430 )431 if "age" in self.label_types:432 label_dict["age"] = torch.LongTensor(433 [self.utt_age_class_dict[u] for u in batch_utts]434 )435 if "age_regression" in self.label_types:436 label_dict["age_regression"] = torch.FloatTensor(437 [self.utt_age_dict[u] for u in batch_utts]438 )439 if "rec" in self.label_types:440 label_dict["rec"] = torch.LongTensor(441 [self.utt_rec_dict[u] for u in batch_utts]442 )443 if "genre" in self.label_types:444 label_dict["genre"] = torch.LongTensor(445 [self.utt_genre_dict[u] for u in batch_utts]446 )447 yield torch.stack(batch_feats), label_dict448 def get_batches_balance(449 self, balance_attribute="speaker", batch_size=256, max_seq_len=400450 ):451 """452 Main data iterator, specify batch_size and max_seq_len453 Specify which attribute to balance454 """455 assert balance_attribute in self.label_types456 if balance_attribute == "speaker":457 self.anchorpool = self.spkrs.copy()458 self.get_utt_method = lambda x: np.random.choice(self.spk_utt_dict[x])459 if balance_attribute == "nationality":460 self.anchorpool = sorted(list(set(self.nats)))461 self.nat_utt_dict = OrderedDict({k: [] for k in self.anchorpool})462 for u in self.utt_list:463 spk = self.utt_spkr_dict[u]464 nat = self.spk_nat_dict[spk]465 self.nat_utt_dict[nat].append(u)466 for n in self.nat_utt_dict:467 self.nat_utt_dict[u] = np.array(self.nat_utt_dict[u])468 self.get_utt_method = lambda x: np.random.choice(self.nat_utt_dict[x])469 if balance_attribute == "age":470 self.anchorpool = sorted(list(set(self.age_classes)))471 self.age_utt_dict = OrderedDict({k: [] for k in self.anchorpool})472 for u in self.utt_age_class_dict:473 nat_class = self.utt_age_class_dict[u]474 self.age_utt_dict[nat_class].append(u)475 for a in self.age_utt_dict:476 self.age_utt_dict[a] = np.array(self.age_utt_dict[a])477 self.get_utt_method = lambda x: np.random.choice(self.age_utt_dict[x])478 lens = [max_seq_len for _ in range(batch_size)]479 while True:480 anchors = np.random.choice(self.anchorpool, size=batch_size)481 batch_utts = [self.get_utt_method(a) for a in anchors]482 batch_fpaths = []483 batch_ids = []484 for utt in batch_utts:485 batch_fpaths.append(self.utt_fpath_dict[utt])486 batch_ids.append(self.utt_spkr_dict[utt])487 if self.asynchr:488 batch_feats = async_map(get_item_train, zip(batch_fpaths, lens))489 else:490 batch_feats = [self.get_item(a) for a in zip(batch_fpaths, lens)]491 label_dict = {}492 if "speaker" in self.label_types:493 label_dict["speaker"] = torch.LongTensor(batch_ids)494 if "nationality" in self.label_types:495 label_dict["nationality"] = torch.LongTensor(496 [self.spk_nat_dict[s] for s in batch_ids]497 )498 if "gender" in self.label_types:499 label_dict["gender"] = torch.LongTensor(500 [self.spk_gen_dict[s] for s in batch_ids]501 )502 if "age" in self.label_types:503 label_dict["age"] = torch.LongTensor(504 [self.utt_age_class_dict[u] for u in batch_utts]505 )506 if "age_regression" in self.label_types:507 label_dict["age_regression"] = torch.FloatTensor(508 [self.utt_age_dict[u] for u in batch_utts]509 )510 if "rec" in self.label_types:511 label_dict["rec"] = torch.LongTensor(512 [self.utt_rec_dict[u] for u in batch_utts]513 )514 if "genre" in self.label_types:515 label_dict["genre"] = torch.LongTensor(516 [self.utt_genre_dict[u] for u in batch_utts]517 )518 yield torch.stack(batch_feats), label_dict519 def get_alldata_batches(self, batch_size=256, max_seq_len=400):520 utt_list = self.utt_list521 start_index = 0522 lens = [max_seq_len for _ in range(batch_size)]523 while start_index <= len(utt_list):524 batch_utts = utt_list[start_index : start_index + batch_size]525 batch_fpaths = []526 batch_ids = []527 for utt in batch_utts:528 batch_fpaths.append(self.utt_fpath_dict[utt])529 batch_ids.append(self.utt_spkr_dict[utt])530 if self.asynchr:531 batch_feats = async_map(get_item_train, zip(batch_fpaths, lens))532 else:533 batch_feats = [self.get_item(a) for a in zip(batch_fpaths, lens)]...
load_dataset.py
Source:load_dataset.py
1import csv2import numpy as np3import os4import sys5import pickle6# BASE PATH DEFINITIONS7DATA_BASE_PATH = '.'8OUTPUT_BASE_PATH = '.'9default_FileName_PositiveInstancesDictionnary = os.path.join(DATA_BASE_PATH, "dictionnaries_and_lists/SmallMolMWFilter_UniprotHumanProt_DrugBank_Dictionary.csv")10default_FileName_ListProt = os.path.join(DATA_BASE_PATH, "dictionnaries_and_lists/list_MWFilter_UniprotHumanProt.txt")11default_FileName_ListMol = os.path.join(DATA_BASE_PATH, "dictionnaries_and_lists/list_MWFilter_mol.txt")12default_FileName_MolKernel = os.path.join(DATA_BASE_PATH, "kernels/kernels.data/Tanimoto_d=8_DrugBankSmallMolMWFilterHuman.data")13default_FileName_DicoMolKernel_indice2instance = os.path.join(DATA_BASE_PATH, "kernels/dict/dico_indice2mol_InMolKernel.data")14default_FileName_DicoMolKernel_instance2indice = os.path.join(DATA_BASE_PATH, "kernels/dict/dico_mol2indice_InMolKernel.data")15def load_dataset(FileName_PositiveInstancesDictionnary=default_FileName_PositiveInstancesDictionnary, FileName_ListProt=default_FileName_ListProt, FileName_ListMol=default_FileName_ListMol, FileName_MolKernel=default_FileName_MolKernel, FileName_DicoMolKernel_indice2instance=default_FileName_DicoMolKernel_indice2instance, FileName_DicoMolKernel_instance2indice=default_FileName_DicoMolKernel_instance2indice):16 """17 Loading the dataset and the molecule kernel18 :param FileName_PositiveInstancesDictionnary: (string) tsv file name: each line corresponds to a molecule; 19 1rst column: gives the DrugBank ID of the molecule20 2nd column: gives the number of targets of the corresponding molecule21 other columns: gives the UniprotIDs of molecule targets (one per column)22 :param FileName_ListProt: (string) txt file name: each line gives the UniprotID of a protein of the dataset23 :param FileName_ListMol: (string) txt file name: each line gives the DrugBankID of a molecule of the dataset24 :param FileName_kernel: (string) pickle file name: contains the molecule kernel (np.array)25 :param FileName_DicoKernel_indice2instance: (string) pickle file name: contains the dictionnary linking indices of the molecule kernel26 to its corresponding molecule ID27 :param FileName_DicoKernel_instance2indice: (string) pickle file name: contains the dictionnary linking molecule IDs to indices 28 in the molecule kernel29 30 :return K_mol: (np.array: number of mol^2) molecule kernel31 :return DicoMolKernel_ind2mol: (dictionnary) keys are indices of the molecule kernel (i.e. integers between 0 and number_of_mol)32 and corresponding values are DrugbankIDS of the molecule corresponding to the index33 :return DicoMolKernel_mol2ind: (dictionnary) keys are DrugbankIDs and values are their corresponding indices of the molecule kernel34 :return interaction_matrix: (np.array: number_of_mol*number_of_prot) array whose values are 1 if the molecule/protein couple35 is interaction or 0 otherwise36 """37 ##loading molecule kernel and its associated dictionnaries38 with open(FileName_MolKernel, 'rb') as fichier:39 pickler = pickle.Unpickler(fichier)40 K_mol = pickler.load().astype(np.float32)41 with open(FileName_DicoMolKernel_indice2instance, 'rb') as fichier:42 pickler = pickle.Unpickler(fichier)43 DicoMolKernel_ind2mol = pickler.load()44 with open(FileName_DicoMolKernel_instance2indice, 'rb') as fichier:45 pickler = pickle.Unpickler(fichier)46 DicoMolKernel_mol2ind = pickler.load()47 48 ##charging protein list of dataset49 list_prot_of_dataset = []50 f_in = open(FileName_ListProt, 'r')51 for line in f_in:52 list_prot_of_dataset.append(line.rstrip())53 f_in.close()54 ##charging list_mol_of_dataset55 list_mol_of_dataset = []56 f_in = open(FileName_ListMol, 'r')57 for line in f_in:58 list_mol_of_dataset.append(line.rstrip())59 f_in.close()60 ##charging list of targets per molecule of the dataset61 #initialization62 dico_targets_per_mol = {}63 for mol in list_mol_of_dataset:64 dico_targets_per_mol[mol] = []65 66 #filling67 f_in = open(FileName_PositiveInstancesDictionnary, 'r')68 reader = csv.reader(f_in, delimiter='\t')69 for row in reader:70 nb_prot = int(row[1])71 for j in range(nb_prot):72 dico_targets_per_mol[row[0]].append(row[2+j])73 del reader74 f_in.close()75 76 ##making interaction_matrix77 interaction_matrix = np.zeros((len(list_mol_of_dataset), len(list_prot_of_dataset)), dtype=np.float32)78 for i in range(len(list_mol_of_dataset)):79 list_of_targets = dico_targets_per_mol[list_mol_of_dataset[i]]80 nb=081 for j in range(len(list_prot_of_dataset)):82 if list_prot_of_dataset[j] in list_of_targets:83 interaction_matrix[i,j] = 184 nb+=185 ###FOR TESTING86 #if len(list_of_targets)!=nb:87 # print("alerte")88 # exit(1)89 90 return K_mol, DicoMolKernel_ind2mol, DicoMolKernel_mol2ind, interaction_matrix91###FOR TESTING 92#K_mol, DicoMolKernel_ind2mol, DicoMolKernel_mol2ind, interaction_matrix = load_dataset(FileName_PositiveInstancesDictionnary, FileName_ListProt, FileName_ListMol, FileName_MolKernel, FileName_DicoMolKernel_indice2instance, FileName_DicoMolKernel_instance2indice)93 94 95 96 97 ...
config.py
Source:config.py
1import os23DATA_BASE_PATH = "dataset"45FILE_NAME1 = os.path.sep.join([DATA_BASE_PATH, "conllpp_dev.txt"])6FILE_NAME2 = os.path.sep.join([DATA_BASE_PATH, "conllpp_test.txt"])7FILE_NAME3 = os.path.sep.join([DATA_BASE_PATH, "conllpp_train.txt"])8910NEW_FILE_NAME1 = os.path.sep.join([DATA_BASE_PATH, "conllpp_dev.csv"])11NEW_FILE_NAME2 = os.path.sep.join([DATA_BASE_PATH, "conllpp_test.csv"])12NEW_FILE_NAME3 = os.path.sep.join([DATA_BASE_PATH, "conllpp_train.csv"])131415UP_FILE_NAME1 = os.path.sep.join([DATA_BASE_PATH, "conllpp_up_dev.csv"])16UP_FILE_NAME2 = os.path.sep.join([DATA_BASE_PATH, "conllpp_up_test.csv"])17UP_FILE_NAME3 = os.path.sep.join([DATA_BASE_PATH, "conllpp_up_train.csv"])181920CHECKPOINT_PATH = "checkpoint"2122CHECKPOINT1 = os.path.sep.join([CHECKPOINT_PATH, "checkpoint_saved.pth"])23CHECKPOINT2 = os.path.sep.join([CHECKPOINT_PATH, "checkpoint.pth"])24CHECKPOINT3 = os.path.sep.join([CHECKPOINT_PATH, "model_scripted.pt"])2526CHECKPOINT4 = os.path.sep.join([CHECKPOINT_PATH, "model_last.pt"])
...
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