Method train

nltk/classify/maxent.py:1499–1552

(cls, train_toks, **kwargs)

class TadmMaxentClassifier(MaxentClassifier):
    @classmethod
    def train(cls, train_toks, **kwargs):
        algorithm = kwargs.get("algorithm", "tao_lmvm")
        trace = kwargs.get("trace", 3)
        encoding = kwargs.get("encoding", None)
        labels = kwargs.get("labels", None)
        sigma = kwargs.get("gaussian_prior_sigma", 0)
        count_cutoff = kwargs.get("count_cutoff", 0)
        max_iter = kwargs.get("max_iter")
        ll_delta = kwargs.get("min_lldelta")

        # Construct an encoding from the training data.
        if not encoding:
            encoding = TadmEventMaxentFeatureEncoding.train(
                train_toks, count_cutoff, labels=labels
            )

        trainfile_fd, trainfile_name = tempfile.mkstemp(
            prefix="nltk-tadm-events-", suffix=".gz"
        )
        weightfile_fd, weightfile_name = tempfile.mkstemp(prefix="nltk-tadm-weights-")

        trainfile = gzip_open_unicode(trainfile_name, "w")
        write_tadm_file(train_toks, encoding, trainfile)
        trainfile.close()

        options = []
        options.extend(["-monitor"])
        options.extend(["-method", algorithm])
        if sigma:
            options.extend(["-l2", "%.6f" % sigma**2])
        if max_iter:
            options.extend(["-max_it", "%d" % max_iter])
        if ll_delta:
            options.extend(["-fatol", "%.6f" % abs(ll_delta)])
        options.extend(["-events_in", trainfile_name])
        options.extend(["-params_out", weightfile_name])
        if trace < 3:
            options.extend(["2>&1"])
        else:
            options.extend(["-summary"])

        call_tadm(options)

        with open(weightfile_name) as weightfile:
            weights = parse_tadm_weights(weightfile)

        os.remove(trainfile_name)
        os.remove(weightfile_name)

        # Convert from base-e to base-2 weights.
        weights *= numpy.log2(numpy.e)

        # Build the classifier
        return cls(encoding, weights)


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