import pickle
import sys; sys.path.insert(0, ".")
import csv; csv.field_size_limit(sys.maxsize)
from wikiclass import assessments, languages
from wikiclass.models import RFTextModel
from wikiclass.features import WikitextAndInfonoise
# Train and test set ("<assessment class>", "text content")
input_file = open("datasets/assessed_revisions.with_text.tsv")
train_set = []
test_set = []
for row in csv.DictReader(input_file, delimiter="\t"):
if row['class'] == "A": continue
if row['is_test'] == "FALSE":
train_set.append((row['text'], row['class']))
else:
test_set.append((row['text'], row['class']))
model = RFTextModel.train(
train_set,
assessments=assessments.WP10,
feature_extractor=WikitextAndInfonoise(languages.get('English'))
)
model.to_file(open("enwiki.rf_text.model", "wb"))
import pickle
import sys
sys.path.insert(0, ".")
import csv
csv.field_size_limit(sys.maxsize)
from wikiclass import assessments, languages
from wikiclass.models import RFTextModel
from wikiclass.features import WikitextAndInfonoise
# Train and test set ("<assessment class>", "text content")
input_file = open("datasets/assessed_revisions.with_text.tsv")
train_set = []
test_set = []
for row in csv.DictReader(input_file, delimiter="\t"):
if row['class'] == "A": continue
if row['is_test'] == "TRUE":
test_set.append((row['text'], row['class']))
model = RFTextModel.from_file(open("enwiki.rf_text.model", "rb"))
results = model.test(test_set)
print(results)
import pickle
import sys;sys.path.insert(0, ".")
from pprint import pprint
from wikiclass.models import RFTextModel
model = RFTextModel.from_file(open("enwiki.rf_text.model", "rb"))
# Classifies a revision of an article based on wikitext alone
text = "An '''anachronism''' {{cite }}(from the [[Ancient Greek|Greek]] <ref ..."
assessment, probs = model.classify(text)
# Print predicted assessment class and probabilities for all classes.
pprint(("assessment", assessment))
pprint(("probs", probs))
import pickle
import sys
sys.path.insert(0, ".")
import csv
csv.field_size_limit(sys.maxsize)
from wikiclass import assessments, languages
from wikiclass.models import RFTextModel
from wikiclass.features import WikitextAndInfonoise
# Train and test set ("<assessment class>", "text content")
input_file = open("datasets/assessed_revisions.with_text.tsv")
train_set = []
test_set = []
for row in csv.DictReader(input_file, delimiter="\t"):
if row['class'] == "A": continue
if row['is_test'] == "FALSE":
train_set.append((row['text'], row['class']))
else:
test_set.append((row['text'], row['class']))
model = RFTextModel.train(train_set,
assessments=assessments.WP10,
feature_extractor=WikitextAndInfonoise(
languages.get('English')))
model.to_file(open("enwiki.rf_text.model", "wb"))