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How to use data_base_path method in pytest-play

Best Python code snippet using pytest-play_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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