class_docstr: Underlying LSTM model for MSTParser used by BIST parser.
class_docstr_tok: ["Underlying","LSTM","model","for","MSTParser","used","by","BIST","parser","."]
code: def train(self, conll_path):
      # pylint: disable=invalid-name
      # pylint: disable=missing-docstring
      eloss = 0.0
      mloss = 0.0
      eerrors = 0
      etotal = 0
      start = time.time()
  
      shuffled_data = list(read_conll(conll_path))
      random.shuffle(shuffled_data)
      errs = []
      lerrs = []
      i_sentence = 0
  
      for sentence in shuffled_data:
          if i_sentence % 100 == 0 and i_sentence != 0:
              print(
                  "Processing sentence number:",
                  i_sentence,
                  "Loss:",
                  eloss / etotal,
                  "Errors:",
                  (float(eerrors)) / etotal,
                  "Time",
                  time.time() - start,
              )
              start = time.time()
              eerrors = 0
              eloss = 0.0
              etotal = 0
  
          conll_sentence = [
              entry
              for entry in sentence
              if isinstance(entry, ConllEntry)
          ]
  
          for entry in conll_sentence:
              c = float(self.words_count.get(entry.norm, 0))
              drop_flag = random.random() < (c / (0.25 + c))
              wordvec = (
                  self.wlookup[
                      int(self.vocab.get(entry.norm, 0))
                      if drop_flag
                      else 0
                  ]
                  if self.wdims > 0
                  else None
              )
              posvec = (
                  self.plookup[int(self.pos[entry.pos])]
                  if self.pdims > 0
                  else None
              )
  
              entry.vec = concatenate(
                  [_f for _f in [wordvec, posvec, None] if _f]
              )
  
              entry.lstms = [entry.vec, entry.vec]
              entry.headfov = None
              entry.modfov = None
  
              entry.rheadfov = None
              entry.rmodfov = None
  
          if self.blstm_flag:
              lstm_forward = self.builders[0].initial_state()
              lstm_backward = self.builders[1].initial_state()
  
              for entry, rentry in zip(
                  conll_sentence, reversed(conll_sentence)
              ):
                  lstm_forward = lstm_forward.add_input(entry.vec)
                  lstm_backward = lstm_backward.add_input(rentry.vec)
  
                  entry.lstms[1] = lstm_forward.output()
                  rentry.lstms[0] = lstm_backward.output()
  
              if self.bibi_flag:
                  for entry in conll_sentence:
                      entry.vec = concatenate(entry.lstms)
  
                  blstm_forward = self.bbuilders[0].initial_state()
                  blstm_backward = self.bbuilders[1].initial_state()
  
                  for entry, rentry in zip(
                      conll_sentence, reversed(conll_sentence)
                  ):
                      blstm_forward = blstm_forward.add_input(entry.vec)
                      blstm_backward = blstm_backward.add_input(
                          rentry.vec
                      )
  
                      entry.lstms[1] = blstm_forward.output()
                      rentry.lstms[0] = blstm_backward.output()
  
          scores, exprs = self._evaluate(conll_sentence)
          gold = [entry.parent_id for entry in conll_sentence]
          heads = decoder.parse_proj(
              scores, gold if self.costaug_flag else None
          )
  
          if self.labels_flag:
              for modifier, head in enumerate(gold[1:]):
                  rscores, rexprs = self._evaluate_label(
                      conll_sentence, head, modifier + 1
                  )
                  gold_label_ind = self.rels[
                      conll_sentence[modifier + 1].relation
                  ]
                  wrong_label_ind = max(
                      (
                          (label, scr)
                          for label, scr in enumerate(rscores)
                          if label != gold_label_ind
                      ),
                      key=itemgetter(1),
                  )[0]
                  if (
                      rscores[gold_label_ind]
                      < rscores[wrong_label_ind] + 1
                  ):
                      lerrs.append(
                          rexprs[wrong_label_ind]
                          - rexprs[gold_label_ind]
                      )
  
          e = sum([1 for h, g in zip(heads[1:], gold[1:]) if h != g])
          eerrors += e
          if e > 0:
              loss = [
                  (exprs[h][i] - exprs[g][i])
                  for i, (h, g) in enumerate(zip(heads, gold))
                  if h != g
              ]  # * (1.0/float(e))
              eloss += e
              mloss += e
              errs.extend(loss)
  
          etotal += len(conll_sentence)
  
          if i_sentence % 1 == 0 or errs > 0 or lerrs:
              if errs or lerrs:
                  eerrs = esum(
                      errs + lerrs
                  )  # * (1.0/(float(len(errs))))
                  eerrs.scalar_value()
                  eerrs.backward()
                  self.trainer.update()
                  errs = []
                  lerrs = []
  
              renew_cg()
  
          i_sentence += 1
  
      if errs:
          eerrs = esum(errs + lerrs)  # * (1.0/(float(len(errs))))
          eerrs.scalar_value()
          eerrs.backward()
          self.trainer.update()
  
          renew_cg()
  
      self.trainer.update()
      print("Loss: ", mloss / i_sentence)
  
file_path: nlp-architect/nlp_architect/models/bist/mstlstm.py
imports: import random
  import time
  from collections import namedtuple
  from operator import itemgetter
  from dynet import ParameterCollection, AdamTrainer, LSTMBuilder, tanh, logistic, rectify, cmult, SimpleRNNBuilder, concatenate, np, renew_cg, esum
  from nlp_architect.data.conll import ConllEntry
  from nlp_architect.models.bist import decoder
  from nlp_architect.models.bist.utils import read_conll
label_desc: Use image node_id 6 for calling the MSTParserLSTM obj's underlying member method code with example usage: obj.train(conll_path) without return types