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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