def get_test_data():
grades = ['1', '3', '6', '9']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/ApiData/rus/ent/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['ent-rus']
features_collection.drop()
for text in dataset:
features = extract_features(text.data.encode('utf-8'))
text_features = {"grade": text.grade, "features": features}
print str(features)
features_collection.insert_one(text_features)
def get_test_data():
grades = ['1', '3', '6', '9']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/rus/word/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['lem-rus']
features_collection.drop()
for text in dataset:
text_features = {"grade": text.grade,
"features": extract_features(text.data)}
features_collection.insert_one(text_features)
def get_test_data():
# grades = ['K-1', '4-5', '9-10']
grades = ['2-3', '6-8', '11-CCR']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/eng/byGrade/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['shallow-eng']
features_collection.drop()
for text in dataset:
print text.grade
text_features = {"grade": text.grade,
"features": extract_features(text.data)}
features_collection.insert_one(text_features)
def get_test_data():
grades = ['1', '3', '6', '9']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/ApiData/rus/ent/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['ent-rus']
features_collection.drop()
for text in dataset:
features = extract_features(text.data.encode('utf-8'))
text_features = {"grade": text.grade, "features": features}
print str(features)
features_collection.insert_one(text_features)
def get_test_data():
grades = ['1', '3', '6', '9']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/rus/word/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['shallow-rus']
features_collection.drop()
for text in dataset:
features = extract_features(text.data)
text_features = {"grade": text.grade, "features": features}
print text.grade
features_collection.insert_one(text_features)
def create_lm(path_to_data, grades, f_type):
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
for grade in grades:
print grade + " grade"
start_total = time.time()
client.drop_database(f_type + '_' + grade)
db = client[f_type + '_' + grade]
for n in xrange(1, 3):
print str(n) + " gram"
start = time.time()
fd_dict = dict()
# TODO separate dataset on grades
for text in dataset:
if text.grade == grade:
fd_dict[text.name] = FreqDist()
fd_dict['all'] = FreqDist()
for text in dataset:
if text.grade == grade:
tokens = nltk.word_tokenize(text.data)
tokens_l = [token.lower() for token in tokens]
for key in fd_dict:
if key != text.name:
n_grams = ngrams(tokens_l, n)
fd_dict[key].update(n_grams)
for key in fd_dict:
lm_collection = db[key]
fd = fd_dict[key]
sgt = SimpleGoodTuringProbDist(fd)
prob_many = list()
for fd_key in fd:
prob_many.append({"type": fd_key, "n-gram": n, "count": fd[fd_key], "prob": sgt.prob(fd_key)})
if prob_many:
lm_collection.insert_many(prob_many)
print str(time.time() - start) + " sec"
print str(time.time() - start_total) + " sec total"
def get_test_data():
grades = ['1', '3', '6', '9']
# grades = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10-11']
# path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_test/rus/word/"
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/rus/word/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['affixes']
features_collection.drop()
for text in dataset:
text_features = {"grade": text.grade,
"features": extract_features(text.data)}
features_collection.insert_one(text_features)
def get_test_data():
# grades = ['K-1', '4-5', '9-10']
grades = ['2-3', '6-8', '11-CCR']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/eng/byGrade/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['shallow-eng']
features_collection.drop()
for text in dataset:
print text.grade
text_features = {
"grade": text.grade,
"features": extract_features(text.data)
}
features_collection.insert_one(text_features)
def get_test_data(f_type, path_to_data, grades):
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features[f_type]
features_collection.drop()
start = time.time()
for text in dataset:
print text.name
text_features = {"grade": text.grade,
"features": extract_features(f_type, text, grades)}
features_collection.insert_one(text_features)
print str(time.time() - start) + " sec total"
def get_test_data():
clf = make_features()
grades = ['1', '3', '6', '9']
# path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_test/rus/word/"
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/rus/word/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['inclusions']
features_collection.drop()
for text in dataset:
features = extract_features(text.data, clf)
text_features = {"grade": text.grade, "features": features}
print str(features)
features_collection.insert_one(text_features)
def get_all_ig(path, grades):
dataset = prepare_dataset(path, grades)
words = prepare_ig_candidates(dataset)
ig_words = dict()
for word in words:
ig_words[word] = calc_ig_value(word, grades, dataset)
sorted_ig_words = sorted(ig_words.items(), key=operator.itemgetter(1), reverse=True)
top_ig_words = list()
for word in sorted_ig_words:
if len(top_ig_words) < 1000:
top_ig_words.append(word[0])
else:
break
return top_ig_words
def get_test_data():
clf = make_features()
grades = ["1", "3", "6", "9"]
# path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_test/rus/word/"
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/rus/word/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient("mongodb://localhost:27017/")
features_collection = client.features["inclusions"]
features_collection.drop()
for text in dataset:
features = extract_features(text.data, clf)
text_features = {"grade": text.grade, "features": features}
print str(features)
features_collection.insert_one(text_features)
def get_test_data(f_type, path_to_data, grades):
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features[f_type]
features_collection.drop()
start = time.time()
for text in dataset:
print text.name
text_features = {
"grade": text.grade,
"features": extract_features(f_type, text, grades)
}
features_collection.insert_one(text_features)
print str(time.time() - start) + " sec total"
def get_all_ig(path, grades):
dataset = prepare_dataset(path, grades)
words = prepare_ig_candidates(dataset)
ig_words = dict()
for word in words:
ig_words[word] = calc_ig_value(word, grades, dataset)
sorted_ig_words = sorted(ig_words.items(),
key=operator.itemgetter(1),
reverse=True)
top_ig_words = list()
for word in sorted_ig_words:
if len(top_ig_words) < 1000:
top_ig_words.append(word[0])
else:
break
return top_ig_words
def get_test_data():
pos_set = [nouns, verbs, adjectives, adverbs, prepositions]
grades = ['1', '3', '6', '9']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/ApiData/rus/pos/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['pos-rus']
features_collection.drop()
for text in dataset:
features = []
for pos in pos_set:
features += extract_features(text.data.encode('utf-8'), pos)
text_features = {"grade": text.grade, "features": features}
print str(features)
features_collection.insert_one(text_features)
def get_test_data():
pos_set = [nouns, verbs, adjectives, adverbs, prepositions]
grades = ['1', '3', '6', '9']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/ApiData/rus/pos/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['pos-rus']
features_collection.drop()
for text in dataset:
features = []
for pos in pos_set:
features += extract_features(text.data.encode('utf-8'), pos)
text_features = {"grade": text.grade, "features": features}
print str(features)
features_collection.insert_one(text_features)
def get_test_data():
pos_set = [nouns, verbs, adverbs, adjectives, prepositions]
# grades = ['K-1', '4-5', '9-10']
grades = ['2-3', '6-8', '11-CCR']
# grades = ['K-1', '2-3', '4-5', '6-8', '9-10', '11-CCR']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/eng/byGrade/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['pos-eng']
features_collection.drop()
for text in dataset:
features = []
for pos in pos_set:
features += extract_features(text.data, pos)
print features
text_features = {"grade": text.grade, "features": features}
features_collection.insert_one(text_features)
def get_test_data():
pos_set = [nouns, verbs, adverbs, adjectives, prepositions]
# grades = ['K-1', '4-5', '9-10']
grades = ['2-3', '6-8', '11-CCR']
# grades = ['K-1', '2-3', '4-5', '6-8', '9-10', '11-CCR']
path_to_data = "/Users/Ivan/PycharmProject/ReadAbility/DataSets_raw/eng/byGrade/"
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
features_collection = client.features['pos-eng']
features_collection.drop()
for text in dataset:
features = []
for pos in pos_set:
features += extract_features(text.data, pos)
print features
text_features = {"grade": text.grade, "features": features}
features_collection.insert_one(text_features)