def add_auth_feature_bigram(auth_):
wakati_path = my_path.project_path()+'src/wakati/{}/'.format(auth_)
booklist = new_booklist[auth_]
bi_num = make_hinsi_bigram(wakati_path, booklist)
bi_per = num_to_per(bi_num)
save_path = my_path.project_path()+'src/feature/bigram_per.txt'
with open(save_path, mode="rb") as f:
res = pickle.load(f)
res[auth_] = bi_per
with open(save_path, mode="wb") as f:
pickle.dump(res, f)
def main(data_opt_):
#最初にprint
if data_opt_ == 'hinsi':
opt = '品詞出現率'
elif data_opt_ == 'bigram':
opt = '品詞2-gram出現率'
print('使用特徴量：'+opt)
#全著者実行するため、name_pairで著者ペアリストを作成
key_pairs = clf_pair.name_pair()
#結果保存の辞書型
all_result = {}
#key_pairs全実行
for pair in key_pairs:
#classify
result, confusion = clf_main.classify(data_opt_, pair, 5, True, 'forest', 'up')
#all_resultに[著者key1-著者key2]のkeyで結果を保存
all_result['-'.join(pair)] = {'res': result, 'con': confusion}
#all_resultをpickleでdump
with open(my_path.project_path()+'classification/result/res_{}_raw.txt'.format(data_opt_), mode="wb") as f:
pickle.dump(all_result, f)
#結果の要旨をテキストで保存する関数
write_res_view(all_result, data_opt_, 5)
def init_feature_location():
feature_path = my_path.project_path()+'src/feature/'
os.mkdir(feature_path)
with open(feature_path+'hinsi_per.txt', mode="wb") as f:
pickle.dump({},f)
with open(feature_path+'bigram_per.txt', mode="wb") as f:
pickle.dump({},f)
def save_file_(list, author):
name = list[0]
list[1] = ' '.join(list[1])
list[2] = ' '.join(list[2])
list = '\n'.join(list[1:])
with open(my_path.project_path()+'src/wakati/{0}/wakati_{1}.txt'.format(author, name), mode='w') as f:
f.write(list)
def add_auth_feature_hinsi(auth_):
#分かち書きの場所
wakati_path = my_path.project_path()+'src/wakati/{}/'.format(auth_)
#著者の新字新仮名作品リスト
booklist = new_booklist[auth_]
#品詞毎の数のCounter
fnum_auth = make_hinsi_num(wakati_path, booklist)
#fnum_authを品詞率に変換
fper_auth = num_to_per(fnum_auth)
#保存先ファイルの元データを取り出し
save_path = my_path.project_path()+'src/feature/hinsi_per.txt'
with open(save_path, mode="rb") as f:
res = pickle.load(f)
#追加するデータを辞書型に追記
res[auth_] = fper_auth
#保存
with open(save_path, mode="wb") as f:
pickle.dump(res, f)
def write_res_view(source_, data_opt_, num=None):
#ファイルにwriteするための配列
write_list = []
#全著者ペアで繰り返し
for k,v in source_.items():
#keyをsplitして著者keyを取り出し、フルネームに変換
authors = k.split('-')
authors = [full_name.fullname_dict[i] for i in authors]
#著者、正答率をwrite用に用意
authors_print = '著者：'+','.join(authors)
score_print = 'score: {:.5f}'.format(v['res']['s'])
#重要度write用の変数imp_print
imp_print = ['特徴量の重要度']
#引数numで止めるためにcountを設定
count = 0
#重要度降順で繰り返し
for key, val in sorted(v['res']['i'].items(), key=lambda x: x[1], reverse=True):
#imp_printにappend
imp_print.append(key+': {:.5f}'.format(val))
#countがnumと一致したら終了
count += 1
if count == num:
break
conf_print = []
confusion_header = ['', 'p_0', 'p_1']
#混同行列用の数値を取得
out_confusion = clf_output.gen_confusion(v['con'])
#全混同行列で繰り返し
for conf in out_confusion:
this_print = []
#予測の誤答率を計算
this_print.append('t_1 / p_0 : {:.5f}'.format(conf[1][1] / (conf[0][1] + conf[1][1])))
this_print.append('t_0 / p_1 : {:.5f}'.format(conf[0][2] / (conf[0][2] + conf[1][2])))
this_print.append('--------------------------')
#この回の数値をconf_printにappend
conf_print.append('\n'.join(this_print))
#ここまで用意したデータをwrite_listにappend
write_list.append(authors_print)
write_list.append(score_print)
write_list.append('\n'.join(imp_print))
write_list.append('\n'.join(conf_print))
#ファイル名指定して保存
with open(my_path.project_path()+'classification/result/res_{}_view.txt'.format(data_opt_), mode="w") as f:
f.write('\n'.join(write_list))
import pickle, sys, os
from collections import Counter
import full_name, my_path
with open(my_path.project_path()+'src/booklist/new_booklist.txt', mode="rb") as f:
new_booklist = pickle.load(f)
#品詞数のcounterを作成
def make_hinsi_num(wakati_path_, booklist_):
r_list = []
for book in booklist_:
with open(wakati_path_+'wakati_'+book+'.txt', mode="r") as f:
hinsi = f.read().split('\n')[1].split()
h_counter = Counter(hinsi)
r_list.append(h_counter)
return r_list
#品詞n-gramのcounterを作成
def make_hinsi_bigram(wakati_path_, booklist_):
#return用
r_list = []
#新字新仮名作品リスト booklist_で繰り返し
for book in booklist_:
#この作品のpath
bookpath = 'wakati_{}.txt'.format(book)
#openして品詞リスト取り出し
with open(wakati_path_+bookpath, mode="r") as f:
hinsi = f.read().split('\n')[1].split()
#bigramのリスト
hinsi_bigram = []
#index,valueを取り出して繰り返し
'感動詞-接続詞',
'感動詞-指示詞',
'感動詞-接頭辞',
'感動詞-連体詞',
'感動詞-感動詞',
]
#-----------------------------------
#sourceのロード
'''
使用時はcopy.deepcopyで読み込み
そのまま使うと上書きされてデータ量が増える
'''
with open(my_path.project_path() +
'classification/source/source_hinsi_per.txt',
mode="rb") as f:
source_data_hinsi = pickle.load(f)
with open(my_path.project_path() +
'classification/source/source_hinsi_bigram.txt',
mode="rb") as f:
source_data_bigram = pickle.load(f)
#-----------------------------------
#指定した2著者のデータ取り出し
#入力：source_data_hinsi/bigram, [auth, auth]
#出力：data（2著者の特徴量のデータ）, label（dataの著者ラベル 前者が0, 後者が1）
def auth_wakati_mkdir(auth_):
#保存先ディレクトリを作成しておく
save_path = my_path.project_path() + 'src/wakati/{0}'.format(auth_)
os.mkdir(save_path)
import pickle, my_path
#特徴量のデータ
source_path = my_path.project_path() + 'src/feature/'
#分類用のデータ保存先
dump_path = my_path.project_path() + 'classification/source/'
with open(source_path + 'hinsi_per.txt', mode="rb") as f:
source_hinsi_dict = pickle.load(f)
with open(source_path + 'bigram_per.txt', mode="rb") as f:
source_bigram_dict = pickle.load(f)
hinsi_label = [
'名詞', '接尾辞', '助詞', '動詞', '特殊', '副詞', '形容詞', '判定詞', '未定義語', '助動詞', '接続詞',
'指示詞', '接頭辞', '連体詞', '感動詞'
]
bigram_label = [
'名詞-名詞',
'名詞-接尾辞',
'名詞-助詞',
'名詞-動詞',
'名詞-特殊',
'名詞-副詞',
'名詞-形容詞',
'名詞-判定詞',
'名詞-未定義語',
'名詞-助動詞',
'名詞-接続詞',
'名詞-指示詞',
