def estimate(news_title):
# 未知のデータ予測
vec = dictionary.doc2bow(M.isMecab(news_title))
print(vec)
pre = list(matutils.corpus2dense([vec], num_terms=len(dictionary)).T[0])
print(pre)
label_predict = estimator.predict(pre)
print (label_predict)
def cleate_dic():
ARTICLE_NAME = ["Computer","Entertainment","Sports","Science","Economy","World",'Politics','Society']
ARTICLE = {"Computer":"","Entertainment":"","Sports":"","Science":"","Economy":"","World":"","Politics":"","Society":""}
ret=[]
noun = ""
for n in ARTICLE_NAME:
f = codecs.open('/Users/Soma/Onedrive/News_Dataset/article'+n+'.txt', 'r')
ARTICLE[n]=f.readlines()
f.close()
for j in ARTICLE[n]:
ret += M.isMecab2(j)
preprocessed_docs = {}
for name in ret:
preprocessed = gensim.parsing.preprocess_string(noun)
preprocessed_docs[noun] = preprocessed
#print name, ":", preprocessed
documents = corpora.Dictionary(ret)
documents.save_as_text('noun_dic.txt')
def cleate_lda_model():
ARTICLE_NAME = ["Computer", "Entertainment", "Sports", "Science", "Economy", "World", "Politics", "Society"]
ARTICLE = {
"Computer": "",
"Entertainment": "",
"Sports": "",
"Science": "",
"Economy": "",
"World": "",
"Politics": "",
"Society": "",
}
dictionary = corpora.Dictionary.load_from_text("noun_dic.txt")
for n in ARTICLE_NAME:
print "\n" + n + " LDA modl cleate..\n"
f = codecs.open("/Users/Soma/Onedrive/News_Dataset/article" + n + ".txt", "r")
ARTICLE[n] = f.readlines()
f.close()
data_train = [dictionary.doc2bow(M.isMecab(j)) for j in ARTICLE[n]]
tfidf_corpus = gensim.corpora.MmCorpus("news_noun_" + n + ".mm")
lda = models.LdaModel(corpus=tfidf_corpus, id2word=dictionary, num_topics=30)
lda.save("model_" + n + ".lda")
def train():
ARTICLE_NAME = ["Computer","Entertainment","Sports","Science","Economy","World",'Politics','Society']
ARTICLE = {"Computer":"","Entertainment":"","Sports":"","Science":"","Economy":"","World":"","Politics":"","Society":""}
ret=[]
data_train = [[] for row in range(8)]
train_num = 0
train_sum = 0
for n in ARTICLE_NAME:
data_range = countline('/Users/Soma/Onedrive/News_Dataset/article'+n+'.txt')
print data_range
train_sum += data_range
for col in range(data_range):
data_train[train_num].append(None)
train_num += 1
print "\nDataset : " + str(train_sum)
label_train = [None for col in range(train_sum)]
article_data = [None for col in range(train_sum)]
cate_num =0
label_num =0
global dictionary
dictionary = corpora.Dictionary.load_from_text('noun_dic.txt')
for n in ARTICLE_NAME:
num2 = 0
f = codecs.open('/Users/Soma/Onedrive/News_Dataset/article'+n+'.txt', 'r')
ARTICLE[n]=f.readlines()
f.close()
for j in ARTICLE[n]:
tmp = dictionary.doc2bow(M.isMecab(j))
data_train[cate_num][num2] = list(matutils.corpus2dense([tmp], num_terms=len(dictionary)).T[0])
label_train[label_num] = n
num2 +=1
label_num += 1
cate_num += 1
ar_num = 0
for n in data_train:
for d in n:
article_data[ar_num]=d
ar_num = ar_num+1
print "\ntrain start!"
print "please wait..\n"
global estimator
"""
#学習器の最適化
print "学習器最適化テスト開始"
tuned_parameters = [{'n_estimators': [10, 30, 50, 70, 90, 110, 130, 150], 'max_features': ['auto', 'sqrt', 'log2', None]}]
clf = GridSearchCV(RandomForestClassifier(), tuned_parameters, cv=2, scoring='accuracy', n_jobs=-1)
clf.fit(article_data, label_train)
print("ベストパラメタを表示")
print(clf.best_estimator_)
print("トレーニングデータでCVした時の平均スコア")
for params, mean_score, all_scores in clf.grid_scores_:
print("{:.3f} (+/- {:.3f}) for {}".format(mean_score, all_scores.std() / 2, params))
y_true, y_pred = label_test_s, clf.predict(data_test_s)
print(classification_report(y_true, y_pred))
"""
# 学習させる
estimator = RandomForestClassifier(n_estimators=train_sum/100)
estimator.fit(article_data, label_train)
print("\n==== 学習データと予測データが一緒の場合 ====")
print(estimator.score(article_data, label_train))
def train():
ARTICLE_NAME = ["Computer","Entertainment","Sports","Science","Economy","World",'Politics','Society']
ARTICLE = {"Computer":"","Entertainment":"","Sports":"","Science":"","Economy":"","World":"","Politics":"","Society":""}
ret=[]
data_train = [[] for row in range(8)]
train_num = 0
train_sum = 0
for n in ARTICLE_NAME:
data_range = countline('/Users/somatakei/Onedrive/News_Dataset/article'+n+'.txt')
print data_range
train_sum += data_range
for col in range(data_range):
data_train[train_num].append(None)
train_num += 1
print "\nDataset : " + str(train_sum)
label_train = [None for col in range(train_sum)]
article_data = [None for col in range(train_sum)]
cate_num =0
label_num =0
global dictionary
dictionary = corpora.Dictionary.load_from_text('noun_dic.txt')
for n in ARTICLE_NAME:
num2 = 0
f = codecs.open('/Users/somatakei/Onedrive/News_Dataset/article'+n+'.txt', 'r')
ARTICLE[n]=f.readlines()
f.close()
for j in ARTICLE[n]:
tmp = dictionary.doc2bow(M.isMecab(j))
data_train[cate_num][num2] = list(matutils.corpus2dense([tmp], num_terms=len(dictionary)).T[0])
label_train[label_num] = n
num2 +=1
label_num += 1
cate_num += 1
ar_num = 0
for n in data_train:
for d in n:
article_data[ar_num]=d
ar_num = ar_num+1
#dicをBoGの形で配列へ挿入
print "---Bag of Words Corpus---"
bow_docs = {}
bow_docs_all_zeros = {}
for name in names:
sparse = dictionary.doc2bow(preprocessed_docs[name])
bow_docs[name] = sparse
dense = vec2dense(sparse, num_terms=len(dct))
print name, ":", dense
bow_docs_all_zeros[name] = all(d == 0 for d in dense)
print "\nall zeros...\n", [name for name in bow_docs_all_zeros if bow_docs_all_zeros[name]]
#LSIモデリング
print "\nlsi modeling.."
names = dictionary.keys()
lsi_docs = {}
num_topics = 2
lsi_model = gensim.models.LsiModel(bow_docs.values(),id2word=dictionary,num_topics=num_topics)
for name in names:
vec = data_train[name]
sparse = lsi_model[vec]
dense = vec2dense(sparse, num_topics)
lsi_docs[name] = sparse
print name, ":", dense
print "\nTopics"
print lsi_model.print_topics()
# 次元削減後のベクトルを正規化(ベクトルの方向が重要)
print "\nunit vectorization.."
unit_vecs = {}
for name in names:
vec = vec2dense(lsi_docs[name], num_topics)
norm = sqrt(sum(num ** 2 for num in vec))
unit_vec = [num / norm for num in vec]
unit_vecs[name] = unit_vec
print name, ":", unit_vec
#SVMで学習させる
print "\ntrain start!"
print "please wait..\n"
global estimator
estimator = SVC()
estimator.fit(article_data, label_train)
print("\n==== 学習データと予測データが一緒の場合 ====")
print(estimator.score(article_data, label_train))
def train():
ARTICLE_NAME = ["Computer", "Entertainment", "Sports", "Science", "Economy", "World", "Politics", "Society"]
ARTICLE = {
"Computer": "",
"Entertainment": "",
"Sports": "",
"Science": "",
"Economy": "",
"World": "",
"Politics": "",
"Society": "",
}
ret = []
data_train = [[] for row in range(8)]
train_num = 0
train_sum = 0
for n in ARTICLE_NAME:
data_range = countline("/Users/Soma/Onedrive/News_Dataset/article" + n + ".txt")
print data_range
train_sum += data_range
for col in range(data_range):
data_train[train_num].append(None)
train_num += 1
print "\nDataset : " + str(train_sum)
label_train = [None for col in range(train_sum)]
article_data = [None for col in range(train_sum)]
cate_num = 0
label_num = 0
global dictionary
dictionary = corpora.Dictionary.load_from_text("noun_dic.txt")
for n in ARTICLE_NAME:
num2 = 0
f = codecs.open("/Users/Soma/Onedrive/News_Dataset/article" + n + ".txt", "r")
ARTICLE[n] = f.readlines()
f.close()
for j in ARTICLE[n]:
tmp = dictionary.doc2bow(M.isMecab(j))
data_train[cate_num][num2] = list(matutils.corpus2dense([tmp], num_terms=len(dictionary)).T[0])
label_train[label_num] = n
num2 += 1
label_num += 1
cate_num += 1
ar_num = 0
for n in data_train:
for d in n:
article_data[ar_num] = d
ar_num = ar_num + 1
print "\ntrain start!"
print "please wait..\n"
global estimator
estimator = RandomForestClassifier()
# 学習させる
estimator.fit(article_data, label_train)
print ("\n==== 学習データと予測データが一緒の場合 ====")
print (estimator.score(article_data, label_train))
ARTICLE_NAME = ["Computer","Entertainment","Sports","Science","Economy","World",'Politics','Society']
ARTICLE = {"Computer":"","Entertainment":"","Sports":"","Science":"","Economy":"","World":"","Politics":"","Society":""}
ret=[]
data_train = [[] for row in range(8)]
train_num = 0
train_sum = 0
for n in ARTICLE_NAME:
data_range = countline('/Users/Soma/Onedrive/News_Dataset/article'+n+'.txt')
train_sum += data_range
for col in range(data_range):
data_train[train_num].append(None)
train_num += 1
label_train = [None for col in range(train_sum)]
article_data = [None for col in range(train_sum)]
num =0
num3 =0
dictionary = corpora.Dictionary.load_from_text('test_dic4.txt')
M.pp(M.isMecab("香川ループ弾「衝撃」と賞賛"))
test=[[]for col in range(6)]
test[0].append("a")
test[0].append("b")
test[0].append("c")
test[1].append("d")
test[1].append("e")
print (test)
"Sports": "",
"Science": "",
"Economy": "",
"World": "",
"Politics": "",
"Society": "",
}
dictionary = corpora.Dictionary.load_from_text("noun_dic.txt")
for n in ARTICLE_NAME:
print "\n" + n + " LDA modl cleate..\n"
f = codecs.open("/Users/Soma/Onedrive/News_Dataset/article" + n + ".txt", "r")
ARTICLE[n] = f.readlines()
f.close()
data_train = [dictionary.doc2bow(M.isMecab(j)) for j in ARTICLE[n]]
tfidf_corpus = gensim.corpora.MmCorpus("news_noun_" + n + ".mm")
lda = models.LdaModel(corpus=tfidf_corpus, id2word=dictionary, num_topics=30)
lda.save("model_" + n + ".lda")
if __name__ == "__main__":
print "cleate_dic.."
# CD.cleate_dic()
cleate_lda_model()
lda = models.LdaModel.load("model_Sports.lda")
lda2 = models.LdaModel.load("model_Computer.lda")
for n in range(30):
M.pp(lda.print_topics(n + 1))
for n in range(30):
M.pp(lda2.print_topics(n + 1))
from sklearn.ensemble import RandomForestClassifier
import simplejson as json
import Test_MeCab as M
text = "iPhone 従来の「カケホーダイプラン」は、国内音声通話が回数の制限なしで無料となるもので、\
月額料金は2年定期契約で2700円だ。それに対してカケホーダイライトプランは月額料金が1700円と1000円分安くなる。\
その条件として５分以内の通話であれば、何回かけても無料だが、５分を超えた場合は30秒あたり20円がかかることとなった。\
ただし「ファミリー割引」に加入していれば家族間通話が無料となるので、家族で申込みする場合は利用したい。\
■データ定額プランは5GBからパケットパックとの組み合わせは、１か月のパケット上限が５GBまでとなる「データMパック」から用意される。\
こちらは月額料金が5000円。インターネット接続サービスの「spモード」300円に加入すれば、利用料金は月額7000円になる。\
ここに携帯電話の購入代金がかかってくる。たとえば『iPhone 6s』16GBモデルをMNPで購入すると、実質負担金は月額432円なので、\
トータル7432円/月で新型iPhoneを使うことができる。「データ通信よりも通話がメインだ」というユーザーなら、\
「カケホーダイプラン」2700円/月に容量2GBの「データSパック」3500円/月を組み合わせる方法もある。\
こちらで『iPhone 6s』16GBモデルをMNPで購入すると、月額6932円となる。"
text2 ="この辺にぃ、おいしいラーメン屋の屋台があるみたいなんですが、行きませんかー？カケホーダイ,iPhone"
ret = M.isMecab(text)
ret2 = M.isMecab(text2)
dictionary = corpora.Dictionary.load_from_text('test_dic.txt')
vec = dictionary.doc2bow(ret)
print(vec)
tmp = dictionary.doc2bow(ret)
dense = list(matutils.corpus2dense([tmp], num_terms=len(dictionary)).T[0])
print(dense)
vec = dictionary.doc2bow(ret2)
print(vec)
tmp = dictionary.doc2bow(ret2)
dense2 = list(matutils.corpus2dense([tmp], num_terms=len(dictionary)).T[0])
