def evaluate(corpus_path, denominator, intercept):
corpus = read_gvfi(corpus_path)
(training, heldout) = split_corpus(corpus, denominator, intercept)
model, _ = train(training, 30, "lbfgs", 2)
raw = predicted_and_actual_outcomes(model, heldout)
# predicteds/desireds will be maps from classes (=outcomes) to sets of
# corpus rows predicted/desired to be in that class:
predicteds = dict()
desireds = dict()
for outcome in non_ignored_classes:
for dictionary in predicteds, desireds:
dictionary[outcome] = set()
num_correct = 0
for (row, (desired, predicted)) in raw.items():
predicteds[predicted].add(row)
desireds[desired].add(row)
if desired == predicted:
num_correct += 1
# TODO: If we weigh by the annotation's trust, we
# should use the annotation's trust here (and
# elsewhere) instead of 1.
accuracy = float(num_correct) / float(len(raw))
precisions = dict()
recalls = dict()
f1s = dict()
for outcome in non_ignored_classes:
reference = predicteds[outcome]
test = desireds[outcome]
precisions[outcome] = nltk.precision(reference, test)
recalls[outcome] = nltk.recall(reference, test)
f1s[outcome] = nltk.f_measure(reference, test) # TODO: feed it the right alpha (third arg) for f1.
return accuracy, precisions, recalls, f1s, raw
def evaluate(corpus_path, denominator, intercept):
corpus = read_gvfi(corpus_path)
(training, heldout) = split_corpus(corpus, denominator, intercept)
model, _ = train(training, 30, 'lbfgs', 2)
raw = predicted_and_actual_outcomes(model, heldout)
# predicteds/desireds will be maps from classes (=outcomes) to sets of
# corpus rows predicted/desired to be in that class:
predicteds = dict()
desireds = dict()
for outcome in non_ignored_classes:
for dictionary in predicteds, desireds:
dictionary[outcome] = set()
num_correct = 0
for (row, (desired, predicted)) in raw.items():
predicteds[predicted].add(row)
desireds[desired].add(row)
if desired == predicted:
num_correct += 1
# TODO: If we weigh by the annotation's trust, we
# should use the annotation's trust here (and
# elsewhere) instead of 1.
accuracy = float(num_correct) / float(len(raw))
precisions = dict()
recalls = dict()
f1s = dict()
for outcome in non_ignored_classes:
reference = predicteds[outcome]
test = desireds [outcome]
precisions[outcome] = nltk.precision(reference, test)
recalls [outcome] = nltk.recall (reference, test)
f1s [outcome] = nltk.f_measure(reference, test) # TODO: feed it the right alpha (third arg) for f1.
return accuracy, precisions, recalls, f1s, raw
#!/usr/bin/env python
"""Gets the named entities in each article. Outputs a pickled dict from
id_article to entity type to a list of entity names.
"""
import nltk
import pickle
from datum import Datum, read_gvfi
from optparse import OptionParser
option_parser = OptionParser()
(options, [corpus_path, out_path]) = option_parser.parse_args()
corpus = read_gvfi(corpus_path)
def named_entities(datum):
"""Return a dict from entity type to a list of named entities of that
type in the given datum.
"""
result = {}
for sent in nltk.sent_tokenize(datum.article_snippet):
for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
if hasattr(chunk, 'node'):
result.setdefault(chunk.node, [])
result[chunk.node].append('_'.join(c[0].lower() for c in chunk.leaves()))
return result
result = dict()
for datums in corpus.values():
print '.',
#!/usr/bin/env python
"""Gets the named entities in each article. Outputs a pickled dict from
id_article to entity type to a list of entity names.
"""
import nltk
import pickle
from datum import Datum, read_gvfi
from optparse import OptionParser
option_parser = OptionParser()
(options, [corpus_path, out_path]) = option_parser.parse_args()
corpus = read_gvfi(corpus_path)
def named_entities(datum):
"""Return a dict from entity type to a list of named entities of that
type in the given datum.
"""
result = {}
for sent in nltk.sent_tokenize(datum.article_snippet):
for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))):
if hasattr(chunk, 'node'):
result.setdefault(chunk.node, [])
result[chunk.node].append('_'.join(c[0].lower()
for c in chunk.leaves()))
return result
result = dict()
