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How to use seq2str method in Robotframework

Best Python code snippet using robotframework

generate.py

Source:generate.py

...81    '''seq_len` indicates the length of the input number.'''82    r = [random.randint(0, 10**seq_len - 1) for _ in xrange(batch_size)]83    t = [2 * a for a in r]84    return mapstr(r, seq_len), mapstr(t, seq_len+1)85def seq2str(seq, sep=','):86    return sep.join(map(str, seq))87def getprioritySort(batch_size, seq_len, low=0, high=10, tostr=False):88    out = np.random.randint(low, high, (batch_size, seq_len))89    inp = [np.random.permutation(list(enumerate(l))).reshape(-1) for l in out]90    if tostr:91        return map(seq2str, inp), map(seq2str, out)92    else:93        return inp, out94def getinput(batch_size, seq_len, low=0, high=10, tostr=False, prepend_seqlen=False):95    '''samples a tensor of integers from `low` to `high`,96    with size (`batch_size`, `seq_len`).97    Return this tensor if not `tostr` else return a list of strings,98    with str_i being the comma-separated version of each row of tensor.'''99    tensor = np.random.randint(low, high, (batch_size, seq_len))100    if prepend_seqlen:101        tensor = np.append([[seq_len]]*batch_size, tensor, axis=1)102    if not tostr:103        return tensor104    else:105        return map(seq2str, tensor)106def getcopy(batched_in):107    return batched_in108def getrepeatCopy(batched_in, n):109    return np.tile(batched_in, n)110def getreverse(batch_in):111    return [s[::-1] for s in batch_in]112def getoddFirst(batch_in):113    return [list(s[::2]) + list(s[1::2]) for s in batch_in]114def _ngramflip(seq, n):115    return sum([list(seq[i+n-1:(i-1 if i > 0 else None):-1])116            for i in range(0, len(seq), n)],117               [])118def getbigramFlip(batch_in):119    '''`batch_in` should be a tensor fo size (batch_size, seq_len)'''120    return [_ngramflip(row, n=2) for row in batch_in]121def getbatch(batch_size, seq_len, task, start=START, end=END, sep=SEP, alphabet=10,122            nrepeat=None):123    gettask = globals()['get' + task]124    def _format(batch_in, batch_out):125        return ','.join([126            ','.join([start] + [seq2str(in_)] + [sep] + [seq2str(out)] + [end])127            for in_, out in izip(batch_in, batch_out)])128    if task in PERM:129        batch_in = getinput(batch_size, seq_len, high=alphabet)130        batch_out = gettask(batch_in)131        return _format(batch_in, batch_out)132    elif task == 'prioritySort':133        batch_in, batch_out = gettask(batch_size, seq_len, high=alphabet)134        samples = []135        for b in range(batch_size):136            sample = []137            for i in range(seq_len):138                pri = batch_in[b][2*i]139                val = batch_in[b][2*i + 1]140                sample += [start] * pri + [str(val)]141            samples.append(','.join([start] + sample + [sep, seq2str(batch_out[b]), end]))142        return ','.join(samples)143    elif task == 'repeatCopy':144        batch_in = getinput(batch_size, seq_len, high=alphabet)145        assert nrepeat is not None, 'nrepeat should not be None'146        batch_out = getrepeatCopy(batch_in, nrepeat)147        return ','.join([148            ','.join([start] * (nrepeat+1) + [seq2str(in_)] + [sep] + [seq2str(out)] + [end])149            for in_, out in izip(batch_in, batch_out)])150    elif task in ARITH:151        batch_in, batch_out = gettask(batch_size, seq_len)152        return ''.join([start + sep.join([in_, out]) + end153            for in_, out in izip(batch_in, batch_out)])154    else:155        raise Exception('unknown task')156def generate_set(batch_size, nbatch, min_len, max_len, task, alphabet=10, tofile=None,157                nrepeat_low=None, nrepeat_high=None):158    '''`min_len` and `max_len` are inclusive bounds on the input size.159    `nrepeat_low` and `nrepeat_high` are inclusive bounds on repeatCopy repeat size.'''160    batches = []161    for _ in xrange(nbatch):162        seq_len = np.random.randint(min_len, max_len+1)...

arguments.py

Source:arguments.py

1import argparse2import data_loader3import os4import subprocess5base_dir = "/projects/ml/TrRosetta/PDB30-20FEB17"6base_torch_dir = base_dir7if not os.path.exists(base_dir):8    # training on blue9    base_dir = "/gscratch/TrRosetta/"10    if os.path.exists("/scratch/torch/hhr"):11        base_torch_dir = "/scratch"12    else:13        base_torch_dir = base_dir14TRUNK_PARAMS = ['n_module', 'n_module_str', 'n_layer', 'd_msa', 'd_msa_full', 'd_pair', 'd_templ',\15                'n_head_msa', 'n_head_pair', 'n_head_templ', 'd_hidden',\16                'r_ff', 'n_resblock', 'p_drop']17SE3_PARAMS = ['num_layers', 'num_channels', 'num_degrees', 'n_heads', 'div', 18              'l0_in_features', 'l0_out_features', 'l1_in_features', 'l1_out_features',19              'num_edge_features']20# absolute path to folder containing this file21script_dir = os.path.dirname(os.path.realpath(__file__))22def get_args():23    parser = argparse.ArgumentParser()24    # i/o parameters 25    io_group = parser.add_argument_group("i/o parameters")26    io_group.add_argument('-indir', default='models/',27            help='input directory for loading model checkpoint')28    io_group.add_argument('-outdir',default='models/',29            help='output directory for dumping model checkpoints')30    io_group.add_argument('-dont_load_ckpt', default=False, action='store_true',31            help='If True, DO NOT Load pretrained checkpoint params into model?')32    io_group.add_argument('--verbose', '-v', default=False, action='store_true',33            help='If True, will print lots of stuff for debugging')34    # training parameters35    train_group = parser.add_argument_group("training parameters")36    train_group.add_argument("-model_name", default=None,37            help="model name for saving")38    train_group.add_argument('-batch_size', type=int, default=1,39            help="Batch size [1]")40    train_group.add_argument('-lr', type=float, default=2.0e-4, 41            help="Learning rate [5.0e-4]")42    train_group.add_argument('-num_epochs', type=int, default=300,43            help="Number of epochs [300]")44    train_group.add_argument("-step_lr", type=int, default=300,45            help="Parameter for Step LR scheduler [300]")46    train_group.add_argument("-port", type=int, default=12319,47            help="PORT for ddp training, should be randomized [12319]")48    train_group.add_argument("-accum", type=int, default=1,49            help="Gradient accumulation when it's > 1 [1]")50    train_group.add_argument("-f_seq2str", type=float, default=0.5, 51            help="Frequency of doing the seq-->str task vs the str-->seq task")52    train_group.add_argument("-fixbb_des_frac", type=float, default=1.01, 53            help="Of the training examples that are fixed BB examples, what fraction\54                    will be purely sequence design, rather than 'hal' task.")55    train_group.add_argument("-hal_mask_low", type=int, default=3,56            help='Smallest number of residues to mask out for a hal example')57    train_group.add_argument("-hal_mask_high", type=int, default=20, 58            help='Largest number of residues to mask out for a hal example')59    train_group.add_argument("-mode", type=str, default='str2seq', choices=['str2seq', 'seq2str', 'mixed'],60            help="training mode for model")61    train_group.add_argument('-mask_low', type=float, default=0.,62            help='Lower bound for amino acid masking fraction')63    train_group.add_argument('-mask_high', type=float, default=1.,64            help='Upper bound for amino acid masking fraction')65    train_group.add_argument('-mask_structure', default=False, action='store_true',66            help='If True, mask out portions of structure during training/val')67    train_group.add_argument('-get_grad_norm', default=False, action='store_true', 68            help='If True, spit out report of norms of gradients with respect to different loss terms')69    # data-loading parameters70    data_group = parser.add_argument_group("data loading parameters")71    data_group.add_argument('-val', default="%s/val_lists/xaa"%base_dir,72            help="Validation list [%s/val_lists/xaa]"%base_dir)73    data_group.add_argument('-maxseq', type=int, default=1000,74            help="Maximum depth of subsampled MSA [1000]")75    data_group.add_argument('-maxtoken', type=int, default=2**16,76            help="Maximum depth of subsampled MSA [2**16]")77    data_group.add_argument('-maxlat', type=int, default=50,78            help="Maximum depth of subsampled MSA [50]")79    data_group.add_argument("-lmin", type=int, default=100,80            help="Lower limit of crop size [100]")81    data_group.add_argument("-lmax", type=int, default=260,82            help="Upper limit of crop size [260]")83    data_group.add_argument("-rescut", type=float, default=3.5,84            help="Resolution cutoff [3.5]")85    data_group.add_argument("-slice", type=str, default="CONT",86            help="How to make crops [CONT (default) / DISCONT]")87    data_group.add_argument("-subsmp", type=str, default="LOG",88            help="How to subsample MSAs [UNI / LOG (default) / CONST]")89    data_group.add_argument('-mintplt', type=int, default=0,90            help="Minimum number of templates to select [0]")91    data_group.add_argument('-maxtplt', type=int, default=10,92            help="maximum number of templates to select [10]")93    data_group.add_argument('-seqid', type=float, default=150.0,94            help="maximum sequence identity cutoff for template selection [150.0]")95    # Trunk module properties96    trunk_group = parser.add_argument_group("Trunk module parameters")97    trunk_group.add_argument('-n_module', type=int, default=4,98            help="Number of iteration blocks without structure [4]")99    trunk_group.add_argument('-n_module_str', type=int, default=4,100            help="Number of iteration blocks with structure [4]")101    trunk_group.add_argument('-n_layer', type=int, default=1,102            help="Number of attention layer for each transformer encoder [1]")103    trunk_group.add_argument('-d_msa', type=int, default=384,104            help="Number of MSA features [384]")105    trunk_group.add_argument('-d_msa_full', type=int, default=64,106            help="Number of MSA features [64]")107    trunk_group.add_argument('-d_pair', type=int, default=288,108            help="Number of pair features [288]")109    trunk_group.add_argument('-d_templ', type=int, default=64,110            help="Number of templ features [64]")111    trunk_group.add_argument('-n_head_msa', type=int, default=12,112            help="Number of attention heads for MSA2MSA [12]")113    trunk_group.add_argument('-n_head_pair', type=int, default=8,114            help="Number of attention heads for Pair2Pair [8]")115    trunk_group.add_argument('-n_head_templ', type=int, default=4,116            help="Number of attention heads for template [4]")117    trunk_group.add_argument("-d_hidden", type=int, default=64,118            help="Number of hidden features for initial structure builder [64]")119    trunk_group.add_argument("-r_ff", type=int, default=4,120            help="ratio for feed-forward network in transformer encoder [4]")121    trunk_group.add_argument("-n_resblock", type=int, default=1,122            help="Number of residual blocks for MSA2Pair [1]")123    trunk_group.add_argument("-p_drop", type=float, default=0.1,124            help="Dropout ratio [0.1]")125    trunk_group.add_argument("-not_use_perf", action="store_true", default=False,126            help="Use performer or not [False]")127    # Structure module properties128    str_group = parser.add_argument_group("structure module parameters")129    str_group.add_argument('-num_layers', type=int, default=3,130            help="Number of equivariant layers in structure module block [3]")131    str_group.add_argument('-num_channels', type=int, default=32,132            help="Number of channels [32]")133    str_group.add_argument('-num_degrees', type=int, default=2,134            help="Number of degrees for SE(3) network [2]")135    str_group.add_argument('-l0_in_features', type=int, default=32,136            help="Number of type 0 input features [32]")137    str_group.add_argument('-l0_out_features', type=int, default=8,138            help="Number of type 0 output features [8]")139    str_group.add_argument('-l1_in_features', type=int, default=3,140            help="Number of type 1 input features [3]")141    str_group.add_argument('-l1_out_features', type=int, default=2,142            help="Number of type 1 output features [2]")143    str_group.add_argument('-num_edge_features', type=int, default=32,144            help="Number of edge features [32]")145    str_group.add_argument('-n_heads', type=int, default=4,146            help="Number of attention heads for SE3-Transformer [4]")147    str_group.add_argument("-div", type=int, default=4,148            help="Div parameter for SE3-Transformer [4]")149    # Loss function parameters150    loss_group = parser.add_argument_group("loss parameters")151    loss_group.add_argument('-w_dist', type=float, default=1.0,152            help="Weight on distd in loss function [1.0]")153    loss_group.add_argument('-w_str', type=float, default=1.0,154            help="Weight on strd in loss function [1.0]")155    loss_group.add_argument('-w_rms', type=float, default=1.0,156            help="Weight on rmsd in loss function [1.0]")157    loss_group.add_argument('-w_lddt', type=float, default=1.0,158            help="Weight on predicted lddt loss [1.0]")159    loss_group.add_argument('-w_blen', type=float, default=0.1,160            help="Weight on predicted blen loss [0.1]")161    loss_group.add_argument('-w_bang', type=float, default=0.1,162            help="Weight on predicted bang loss [0.1]")163    loss_group.add_argument('-w_aa', type=float, default=1.0,164            help="Weight on masked language task")165    # parse arguments166    args = parser.parse_args()167    # Setup dataloader parameters:168    loader_param = data_loader.set_data_loader_params(args)169    # make dictionary for each parameters170    trunk_param = {}171    for param in TRUNK_PARAMS:172        trunk_param[param] = getattr(args, param)173    if not args.not_use_perf:174        trunk_param["performer_N_opts"] = {"nb_features": 64, "feature_redraw_interval": 10000}175        trunk_param["performer_L_opts"] = {"nb_features": 64, "feature_redraw_interval": 10000}176    SE3_param = {}177    for param in SE3_PARAMS:178        if hasattr(args, param):179            SE3_param[param] = getattr(args, param)180    trunk_param['SE3_param'] = SE3_param 181    182    # loss function parameters 183    loss_param = {}184    seq2str_param = {}185    str2seq_param = {}186    187    # take in seq2str loss params from cmd line 188    for param in ['w_dist', 'w_str', 'w_rms', 'w_lddt', 'w_blen', 'w_bang', 'w_aa']:189        seq2str_param[param] = getattr(args, param)190    191    # take care of str2seq params here192    #str2seq_param = {193    #            'w_aa'  : 1.0,194    #            'w_str' : 0.5,195    #            'w_bang': 0.05, 196    #            'w_blen': 0.05, 197    #            'w_dist': 0.05,198    #            'w_lddt': 0.05,199    #            'w_rms' : 0.05,200    #        }201    202    # TODO: Make single set of params203    mixed_param = {204                'w_aa'  : args.w_aa,205                'w_str' : args.w_str,206                'w_bang': args.w_bang,207                'w_blen': args.w_blen,208                'w_dist': args.w_dist,209                'w_lddt': args.w_lddt,210                'w_rms' : 0.05, # IGNORE211            }212    # put both into loss param 213    #loss_param['str2seq'] = str2seq_param214    #loss_param['seq2str'] = seq2str_param215    loss_param['str2seq'] = mixed_param216    loss_param['seq2str'] = mixed_param217    # add git hash of current commit218    args.commit = subprocess.check_output(f'git --git-dir {script_dir}/../../.git rev-parse HEAD',219                                          shell=True).decode().strip()220    with open(os.path.join(args.outdir, 'args.txt'), 'w') as fp:221        fp.write('Arguments from training session\n')222        for key,val in args.__dict__.items():223            fp.write(': '.join([str(key), str(val)])+'\n')...

reserver.py

Source:reserver.py

...70            contentType = "text/plain;charset=utf-8"7172        self.sendResponseWithOutput(response, contentType, out)7374    def _seq2str(self, seq):75        """76        converts sequence of strings or tuples to string to be returned77        to AJAX call78        """79        out = u''80        for toplevel in seq:81            if toplevel is not None:82                if type(toplevel) in types.StringTypes:83                    toplevel = (toplevel, )84                out += u', '.join(85                    ["'%s'" % x.replace("'", "\\'") for x in toplevel])86                out += '\n'87        return out8889    def _dict2str(self, d):90        """91        converts dict to string to be returned to AJAX call92        """93        return u''.join([u"%s: '%s'\n" % (k, v) for k, v in d.items()])9495    def do_re(self, *args, **params):96        """97        return self._dict2str(params) + '\n' + self._seq2str(params.items())98        """99        txt = unicode(params['txt'], 'utf-8')100        regex = unicode(params['regex'], 'utf-8')101        method = params['method']102        options = re.UNICODE103104        optionList = (105             ('dotall', re.DOTALL),106             ('ignorecase', re.IGNORECASE),107             ('multiline', re.MULTILINE),108             ('verbose', re.VERBOSE))109110        for s, bitflag in optionList:111            if params.get(s, '') == 'true':112                options |= bitflag113114        try:115            cre = re.compile(regex, options)116        except Exception, e:117            out = u'ERROR: %s' % e118        else:119            results = getattr(cre, method)(txt)120            121            if results:122                # list result123                if type(results) == types.ListType:124                    out = self._seq2str(results)125                # match object126                else:127                    out = u'[TEXT MATCHES]\n'128                    ng = self._dict2str(results.groupdict())129                    if ng:130                        out += u'### NAMED GROUPS ###\n%s' % ng131                    g = self._seq2str(results.groups())132                    if g:133                        out += u'### GROUPS ###\n%s' % g134            else:135                out = u'[NO MATCH OR NOTHING FOUND]'136        return out137138    def do_POST(self):139        """140        test a re141        """142        length = int(self.headers.getheader('content-length'))        143        qs = self.rfile.read(length)144        params = dict(cgi.parse_qsl(qs, keep_blank_values=1))145        out = self.do_re(**params)
...

distance.py

Source:distance.py

1"""2Distance module3Find distance between sequences4Written by Marshall Beddoe <mbeddoe@baselineresearch.net>5Copyright (c) 2004 Baseline Research6Licensed under the LGPL7"""8#9# Note: Gaps are denoted by the integer value 256 as to avoid '_' problems10#11import align, zlib12from Numeric import *13__all__ = [ "Distance", "Entropic", "PairwiseIdentity", "LocalAlignment" ]14class Distance:15    """Implementation of classify base class"""16    def __init__(self, sequences):17        self.sequences = sequences18        self.N = len(sequences)19        # NxN Distance matrix20        self.dmx = zeros((self.N, self.N), Float)21        for i in range(len(sequences)):22            for j in range(len(sequences)):23                self.dmx[i][j] = -124        self._go()25    def __repr__(self):26        return "%s" % self.dmx27    def __getitem__(self, i):28        return self.dmx[i]29    def __len__(self):30        return len(self.dmx)31    def _go(self):32        """Perform distance calculations"""33        pass34class Entropic(Distance):35    """Distance calculation based off compression ratios"""36    def _go(self):37        # Similarity matrix38        similar = zeros((self.N, self.N), Float)39        for i in range(self.N):40            for j in range(self.N):41                similar[i][j] = -142        #43        # Do compression ratio calculations44        #45        for i in range(self.N):46            for j in range(self.N):47                if similar[i][j] >= 0:48                    continue49                seq1 = self.sequences[i][1]50                seq2 = self.sequences[j][1]51                # Convert sequences to strings, gaps denoted by '_'52                seq1str = ""53                for x in seq1:54                    if x == 256:55                        seq1str += '_'56                    else:57                        seq1str += chr(x)58                seq2str = ""59                for x in seq2:60                    if x == 256:61                        seq2str += '_'62                    else:63                        seq2str += chr(x)64                comp1 = zlib.compress(seq1str)65                comp2 = zlib.compress(seq2str)66                if len(comp1) > len(comp2):67                    score = len(comp2) * 1.0 / len(comp1) * 1.068                else:69                    score = len(comp1) * 1.0 / len(comp2) * 1.070                similar[i][j] = similar[j][i] = score71        #72        # Distance matrix73        #74        for i in range(self.N):75            for j in range(self.N):76                self.dmx[i][j] = similar[i][i] - similar[i][j]77class PairwiseIdentity(Distance):78    """Distance through basic pairwise similarity"""79    def _go(self):80        # Similarity matrix81        similar = zeros((self.N, self.N), Float)82        for i in range(self.N):83            for j in range(self.N):84                similar[i][j] = -185        #86        # Find pairs87        #88        for i in range(self.N):89            for j in range(self.N):90                if similar[i][j] >= 0:91                    continue92                seq1 = self.sequences[i][1]93                seq2 = self.sequences[j][1]94                minlen = min(len(seq1), len(seq2))95                len1 = len2 = idents = 096                for x in range(minlen):97                    if seq1[x] != 256:98                        len1 += 1.099                        if seq1[x] == seq2[x]:100                            idents += 1.0101                    if seq2[x] != 256:102                        len2 += 1.0103                m = max(len1, len2)104                similar[i][j] = idents / m105        #106        # Distance matrix107        #108        for i in range(self.N):109            for j in range(self.N):110                self.dmx[i][j] = similar[i][i] - similar[i][j]111class LocalAlignment(Distance):112    """Distance through local alignment similarity"""113    def __init__(self, sequences, smx=None):114        self.smx = smx115        # If similarity matrix is None, make a quick identity matrix116        if self.smx == None:117            self.smx = zeros((257, 257), Float)118            for i in range(257):119                for j in range(257):120                    if i == j:121                        self.smx[i][j] = 1.0122                    else:123                        self.smx[i][j] = 0.0124        Distance.__init__(self, sequences)125    def _go(self):126        # Similarity matrix127        similar = zeros((self.N, self.N), Float)128        for i in range(self.N):129            for j in range(self.N):130                similar[i][j] = -1131        #132        # Compute similarity matrix of SW scores133        #134        for i in range(self.N):135            for j in range(self.N):136                if similar[i][j] >= 0:137                    continue138                seq1 = self.sequences[i][1]139                seq2 = self.sequences[j][1]140                (nseq1, nseq2, edits1, edits2, score, gaps) = \141                    align.SmithWaterman(seq1, seq2, self.smx, 0, 0)142                similar[i][j] = similar[j][i] = score143        #144        # Compute distance matrix of SW scores145        #146        for i in range(self.N):147            for j in range(self.N):148                if self.dmx[i][j] >= 0:149                    continue150                self.dmx[i][j] = 1 - (similar[i][j] / similar[i][i])...

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