Beispiel #1
0
def em(rules, hypergraphs, group_getter, iters=10):
    """
  Runs the Expectation-Maximization for parse forests on the given data.
  `hypergraphs` is a set of (pre-parsed) training examples where each node has
  been labeled with one of the rules in `rules`. `group-getter` takes a rule
  object as input and returns an object (typically the rule RHS symbol) shared
  by a group of rules whose probabilities should be constrained to sum to 1.
  """

    # precompute normalization groups
    normalizing_groups = defaultdict(list)
    for rule in rules:
        normalizing_groups[group_getter(rule)].append(rule)

    # compute initial weights
    unnormalized_weights = {}
    for rule in rules:
        unnormalized_weights[rule] = 0
    weights = {}
    for key in unnormalized_weights:
        norm = mu.logspace_sum([
            unnormalized_weights[k]
            for k in normalizing_groups[group_getter(key)]
        ])
        weights[key] = mu.logspace_prod([unnormalized_weights[key], -norm])

    logging.info('Running EM for %d iterations.', iters)

    # run EM
    for i in range(iters):

        def scorer(label):
            pr = weights[label]
            return (pr, {label: pr})

        semiring = ExpectationSemiring()

        prob = 0
        counts = {}
        for hg in hypergraphs:
            hg.inside(scorer, semiring)
            prob_single, counts_single = hg.alpha
            prob = mu.logspace_prod([prob, prob_single])
            ncounts = du.d_logspace_scalar_prod(-prob_single, counts_single)
            counts = du.d_logspace_sum([counts, ncounts])

        weights = {}
        for key in counts:
            norm = mu.logspace_sum([
                counts[k] for k in normalizing_groups[group_getter(key)]
                if k in counts
            ])
            weights[key] = mu.logspace_prod([counts[key], -norm])

        logging.info('Iteration %d. NLL: %f.', i, prob)
Beispiel #2
0
def em(rules, hypergraphs, group_getter, iters=10):
  """
  Runs the Expectation-Maximization for parse forests on the given data.
  `hypergraphs` is a set of (pre-parsed) training examples where each node has
  been labeled with one of the rules in `rules`. `group-getter` takes a rule
  object as input and returns an object (typically the rule RHS symbol) shared
  by a group of rules whose probabilities should be constrained to sum to 1.
  """

  # precompute normalization groups
  normalizing_groups = defaultdict(list)
  for rule in rules:
    normalizing_groups[group_getter(rule)].append(rule)

  # compute initial weights
  unnormalized_weights = {}
  for rule in rules:
    unnormalized_weights[rule] = 0
  weights = {}
  for key in unnormalized_weights:
    norm = mu.logspace_sum([unnormalized_weights[k] for k in
                            normalizing_groups[group_getter(key)]])
    weights[key] = mu.logspace_prod([unnormalized_weights[key], -norm])

  logging.info('Running EM for %d iterations.', iters)

  # run EM
  for i in range(iters):
    def scorer(label):
      pr = weights[label]
      return (pr, {label: pr})
    semiring = ExpectationSemiring()

    prob = 0
    counts = {}
    for hg in hypergraphs:
      hg.inside(scorer, semiring)
      prob_single, counts_single = hg.alpha
      prob = mu.logspace_prod([prob, prob_single])
      ncounts = du.d_logspace_scalar_prod(-prob_single, counts_single)
      counts = du.d_logspace_sum([counts, ncounts])

    weights = {}
    for key in counts:
      norm = mu.logspace_sum([counts[k] for k in
                              normalizing_groups[group_getter(key)]
                              if k in counts])
      weights[key] = mu.logspace_prod([counts[key], -norm])

    logging.info('Iteration %d. NLL: %f.', i, prob)
Beispiel #3
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  def prod_op(self, values):
    exps = [ex for pr,ex in values]
    for i in range(len(values)):
      pr,ex = values[i]
      for j in range(len(exps)):
        if i == j:
          continue
        exps[j] = du.d_logspace_scalar_prod(pr, exps[j])

    return (mu.logspace_prod([pr for pr,ex in values]),
            du.d_logspace_sum(exps))
Beispiel #4
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    def prod_op(self, values):
        exps = [ex for pr, ex in values]
        for i in range(len(values)):
            pr, ex = values[i]
            for j in range(len(exps)):
                if i == j:
                    continue
                exps[j] = du.d_logspace_scalar_prod(pr, exps[j])

        return (mu.logspace_prod([pr for pr, ex in values]),
                du.d_logspace_sum(exps))