class Benchmarker:
def __init__(self):
self.mongoCollection = MongoCollection()
def benchmark(self, classes, classification_method, fields):
all_records = self.mongoCollection.get_all_records()
if all_records < 2:
return
print reduce(
lambda res, class_name: update_and_return_json(
res, class_name, self.create_accuracy_ranking(all_records, fields, class_name, classification_method)
),
classes,
{},
)
def create_accuracy_ranking(self, records, features_to_benchmark, class_name, delivered_classification_method):
def increment_if_successful_classified(score, record, feature):
if record["classes"][class_name] == delivered_classification_method(
record, class_name, feature, [x for x in records if x != record]
):
score += 1
return score
return reduce(
lambda result, feature: update_and_return_json(
result,
feature,
reduce(lambda score, record: increment_if_successful_classified(score, record, feature), records, 0)
/ (len(records) - 1),
),
features_to_benchmark,
{},
)
class Benchmarker:
def __init__(self):
self.mongoCollection = MongoCollection()
def benchmark(self, classes, classification_method, fields):
all_records = self.mongoCollection.get_all_records()
if all_records < 2:
return
print reduce(
lambda res, class_name: update_and_return_json(
res, class_name,
self.create_accuracy_ranking(all_records, fields, class_name,
classification_method)), classes,
{})
def create_accuracy_ranking(self, records, features_to_benchmark,
class_name, delivered_classification_method):
def increment_if_successful_classified(score, record, feature):
if record['classes'][
class_name] == delivered_classification_method(
record, class_name, feature,
[x for x in records if x != record]):
score += 1
return score
return reduce(
lambda result, feature: update_and_return_json(
result, feature,
reduce(
lambda score, record: increment_if_successful_classified(
score, record, feature), records, 0) /
(len(records) - 1)), features_to_benchmark, {})
class ClassifierAggregator:
def __init__(self):
self.mongoCollection = MongoCollection()
def full_classification(self, to_detect_position):
def proceed_classification_for_class_name(class_name):
print "Classifying for", class_name
with_class_records = self.mongoCollection.get_positons_with_given_class(class_name)
# class_names = map(lambda x: x["classes"][class_name], with_class_records)
t0 = time()
numerical_classification = NumericalFeaturesClassifier.classify(to_detect_position,
class_name,
numerical_characteristics,
with_class_records)
print "Numerical classification counted in %fs" % (time() - t0)
t0 = time()
parts_of_speech_classification = PartsOfSpeechClassifier.classify(to_detect_position,
class_name,
"parts_of_speech_frequencies",
with_class_records)
print "Parts of speech classification counted in %fs" % (time() - t0)
t0 = time()
# most_common_words_class = CommonWordsClassifier.classify(to_detect_position,
# "top_words",
# class_name,
# with_class_records)
print "Common words classification counted in %fs" % (time() - t0)
return {
class_name: {
"numerical_classification": numerical_classification[0],
"parts_of_speech_frequencies_classification": parts_of_speech_classification[0],
"top_common_words": "no be defined "
}
}
return map(proceed_classification_for_class_name, to_detect_position["classes"])
def __init__(self):
self.mongoCollection = MongoCollection()
from parser.MongoCollection import MongoCollection
from parser.classification.ClassifierAggregator import ClassifierAggregator
mongoCollection = MongoCollection()
def test_classification():
to_detect = mongoCollection.get_first_from_db()
to_detect["classes"] = {
"author": "true",
"type": "true",
"age": "false",
"male": "false"
}
return ClassifierAggregator().full_classification(to_detect)
def __init__(self):
self.mongoCollection = MongoCollection()
