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How to use validate_lazy method in pandera

Best Python code snippet using pandera_python

__init__.py

Source:__init__.py

...82  def __init__(self, params):83    self.json = params84    85    if self.is_lazy(params):86      self.validate_lazy(params)87      self.initialise_lazy(params)88    89    self.validate()90  91  def validate_lazy(self, params):92    for key in ForceModel.compulsory_keys_lazy:93      try:94        params[key]95      except KeyError:96        raise InvalidArgumentError("Compulsory property '" + key + "' was not specified.")97  98  def initialise_lazy(self, params):99    if params['type'] == ForceModelType.HOOKIAN:100      self.initialise_lazy_hookian(params)101    else:102      s = """hookian force model initialisation is the only type to have been103      implemented in the wrapper so far - you may specify your own herzian 104      spring/damping values."""105      raise Exception(s)...

pgn.py

Source:pgn.py

...34    @pgn.setter35    def pgn(self, pgn):36        """Check and set the pgn property."""37        pgn = re.sub(r'\ +', ' ', pgn.strip())38        self.validate_lazy(pgn)39        self._pgn = pgn40        self._parse()41    def _parse(self):42        moves = ''43        for line in self.pgn.split("\n"):44            line = line.strip()45            if line.startswith('['):46                line = line.strip("[]").split(' ')47                attr = line[0].lower().strip()48                value = " ".join(line[1:]).strip().strip('"')49                if attr not in self._VALID_TAGS and not self.lazy:50                    message = 'Tags "{0}" not in valid tags list: {1}'51                    raise ValueError(message.format(attr,52                                                    str(self._VALID_TAGS)))53                # set in tags dict for safe access of tags54                self.tags[attr] = value55                # set as attribute of object for simple access of known tags56                setattr(self, attr, value)57            else:  # moves58                if line != '':59                    moves += line + ' '60        self.moves_string = re.sub(r'\s+', ' ', moves.strip())61    @staticmethod62    def validate_lazy(pgn):63        """Validate the pgn string."""64        if not pgn.startswith("[Event"):65            raise TypeError("Not a valid PGN. Doesn't start with Event attr.")66def get_pgns(pgn_string):67    """Return a list of Game instance based on the pgn string."""68    pgns = []69    current_pgn = ''70    for line in pgn_string.strip().split("\n"):71        if line.startswith('[Event'):72            if current_pgn != '':73                pgn = Game(current_pgn)74                pgns.append(pgn)75                current_pgn = ''76        current_pgn += line + "\n"...

run.py

Source:run.py

1#!/usr/bin/env python 2import os3import json as js4import argparse as argp5import pickle as pk6import torch as tc7import torch.nn as nn8from torch.utils.data import DataLoader9from localatt.get_class_weight import get_class_weight10from localatt.lld_dataset import lld_dataset11from localatt.lld_dataset import lld_collate_fn12from localatt.validate_lazy import validate_war_lazy 13from localatt.validate_lazy import validate_uar_lazy14from localatt.localatt import localatt15from localatt.train import train16from localatt.train import validate_loop_lazy17device=tc.device("cuda:0" if tc.cuda.is_available() else "cpu")18pars = argp.ArgumentParser()19pars.add_argument('--propjs', help='property json')20with open(pars.parse_args().propjs) as f:21    #p = js.load(f.read())22    p = js.load(f)23print(js.dumps(p, indent=4))24with open(p['fulldata'], 'rb') as f:25    full_utt_lld = pk.load(f)26train_utt_lab_path    = p['dataset']+'/train_utt_lab.list'27dev_utt_lab_path      = p['dataset']+'/dev_utt_lab.list'28eval_utt_lab_path      = p['dataset']+'/eval_utt_lab.list'29model_pth    =  p['model']30log          =  p['log']31lr=p['lr']32ephs=p['ephs']33bsz=p['bsz']34    35with open(p['dataset']+'/idx_label.json') as f:36    tgt_cls = js.load(f) # target classes37featdim=p['featdim']38nhid=p['nhid']39measure=p['measure']40ncell=p['ncell']41nout = len(tgt_cls)42cls_wgt = get_class_weight(train_utt_lab_path,device) # done.43print('class weight:', cls_wgt)44valid_lazy = {'uar': validate_uar_lazy,45                'war': validate_war_lazy } # done.46# loading47trainset = lld_dataset(full_utt_lld, train_utt_lab_path, cls_wgt, device)48devset = lld_dataset(full_utt_lld, dev_utt_lab_path, cls_wgt, device)49evalset = lld_dataset(full_utt_lld, eval_utt_lab_path, cls_wgt, device)50_collate_fn = lld_collate_fn(device=device) # done.51trainloader = DataLoader(trainset, bsz, collate_fn=_collate_fn)52devloader = DataLoader(devset, bsz, collate_fn=_collate_fn)53evalloader = DataLoader(evalset, bsz, collate_fn=_collate_fn)54# training55model = localatt(featdim, nhid, ncell, nout) # done.56model.to(device)57print(model)58crit = nn.CrossEntropyLoss(weight=cls_wgt)59if os.path.exists(model_pth):60    print(model_pth, 'already exists')61else:62    optim = tc.optim.Adam(model.parameters(), lr=0.00005)63    _val_lz = valid_lazy[measure](crit=crit)64    _val_loop_lz = validate_loop_lazy(name='valid', 65            loader=devloader,log=log)66    trained = train(model, trainloader, 67            _val_lz, _val_loop_lz, crit, optim, ephs, log)68    tc.save(trained.state_dict(), model_pth)69model.load_state_dict(tc.load(model_pth))70_val_lz = valid_lazy[measure](model=model, crit=crit)71# testing...

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