def title_cluster(df, save_df=False):
"""按新闻标题聚类"""
df_title = df.copy()
df_title = title_preprocess(df_title)
word_library_list = counter.get_word_library(df_title['title_cut'])
single_frequency_words_list = counter.get_single_frequency_words(df_title['title_cut'])
max_features = len(word_library_list) - len(single_frequency_words_list) // 2
title_matrix = modeling.feature_extraction(df_title['title_'], vectorizer='CountVectorizer',
vec_args={'max_df': 1.0, 'min_df': 1, 'max_features': max_features})
title_dbscan = modeling.get_cluster(title_matrix, cluster='DBSCAN',
cluster_args={'eps': 0.4, 'min_samples': 4, 'metric': 'cosine'})
title_labels = modeling.get_labels(title_dbscan)
df_title['title_label'] = title_labels
df_non_outliers = modeling.get_non_outliers_data(df_title, label_column='title_label')
title_label_num = counter.get_num_of_value_no_repeat(df_non_outliers['title_label'].tolist())
print('按新闻标题聚类，一共有%d个簇(不包括离群点)' % title_label_num)
title_rank = modeling.label2rank(title_labels)
df_title['title_rank'] = title_rank
for i in range(1, title_label_num + 1):
df_ = df_title[df_title['title_rank'] == i]
title_top_list = counter.get_most_common_words(df_['title_cut'], top_n=10)
print(title_top_list)
if save_df:
df_title.drop(['content', 'title_', 'title_label'], axis=1, inplace=True)
news_crawler.save_news(df_title, os.path.join(results_path, 'df_title_rank.csv'))
return df_title
def show_word_pie():
word_df = news_pandas.load_news(os.path.join(results_path, 'word_df.csv'))
word_df['wordvec'] = word_df['wordvec'].map(eval)
n_clusters = counter.get_num_of_value_no_repeat(word_df['word_label'])
word_label_value = [word_df[word_df['word_label'] == i].shape[0] for i in range(n_clusters)]
word_label_yticks = [word_df[word_df['word_label'] == i]['word'][:5].tolist() for i in range(n_clusters)]
drawing.draw_clustering_analysis_pie(n_clusters, word_label_value, word_label_yticks)
def content_cluster(df, df_save=False):
"""按新闻内容聚类"""
df_content = df.copy()
df_content = content_preprocess(df_content)
word_library_list = counter.get_word_library(df_content['content_cut'])
single_frequency_words_list = counter.get_single_frequency_words(df_content['content_cut'])
max_features = len(word_library_list) - len(single_frequency_words_list) // 2
content_matrix = modeling.feature_extraction(df_content['content_'], vectorizer='CountVectorizer',
vec_args={'max_df': 0.95, 'min_df': 1, 'max_features': max_features})
content_dbscan = modeling.get_cluster(content_matrix, cluster='DBSCAN',
cluster_args={'eps': 0.35, 'min_samples': 4, 'metric': 'cosine'})
content_labels = modeling.get_labels(content_dbscan)
df_content['content_label'] = content_labels
df_non_outliers = modeling.get_non_outliers_data(df_content, label_column='content_label')
content_label_num = counter.get_num_of_value_no_repeat(df_non_outliers['content_label'].tolist())
print('按新闻内容聚类，一共有%d个簇(不包括离群点)' % content_label_num)
content_rank = modeling.label2rank(content_labels)
df_content['content_rank'] = content_rank
for i in range(1, content_label_num + 1):
df_ = df_content[df_content['content_rank'] == i]
content_top_list = counter.get_most_common_words(df_['content_cut'], top_n=15, min_frequency=1)
print(content_top_list)
if df_save:
df_content.drop(['content_', 'content_label'], axis=1, inplace=True)
news_crawler.save_news(df_content, os.path.join(results_path, 'df_content_rank.csv'))
return df_content
def show_hot_barh():
try:
df_non_outliers = news_pandas.load_news(
os.path.join(results_path, 'news_non_outliers.csv'))
df_non_outliers['content_cut'] = df_non_outliers['content_cut'].map(
eval)
except FileNotFoundError:
messagebox.showinfo('Message', '请先对新闻内容文本进行聚类！')
return
rank_num = counter.get_num_of_value_no_repeat(df_non_outliers['rank'])
value = [
df_non_outliers[df_non_outliers['rank'] == i].shape[0]
for i in range(1, rank_num + 1)
]
yticks1 = [
str(
counter.get_most_common_words(
df_non_outliers[df_non_outliers['rank'] == i]['content_cut'],
top_n=10)) + str(i) for i in range(1, rank_num + 1)
]
# yticks2 = [modeling.get_key_sentences('\n'.join(df_non_outliers[df_non_outliers['rank'] == i]['title_']),
# num=1) for i in range(1, rank_num + 1)]
drawing.draw_clustering_analysis_barh(rank_num,
value,
yticks1,
title='热点新闻分布饼图')
def get_key_words():
df_title = news_crawler.load_news(
os.path.join(results_path, 'df_title_rank.csv'))
df_content = news_crawler.load_news(
os.path.join(results_path, 'df_content_rank.csv'))
df_title['title_cut'] = df_title['title_cut'].map(eval)
df_content['content_cut'] = df_content['content_cut'].map(eval)
df_title_content = df_title.copy()
df_title_content['content_cut'] = df_content['content_cut']
df_title_content['content_rank'] = df_content['content_rank']
df_title_content = modeling.get_non_outliers_data(
df_title_content, label_column='title_rank')
title_rank_num = counter.get_num_of_value_no_repeat(
(df_title_content['title_rank']))
for i in range(1, title_rank_num + 1):
df_i = df_title_content[df_title_content['title_rank'] == i]
title = '\n'.join(df_i['title'].tolist())
title = modeling.get_key_sentences(title, num=1)
print('热点：', title)
content_rank = [k for k in df_i['content_rank']]
content_rank = set(content_rank)
for j in content_rank:
df_j = df_i[df_i['content_rank'] == j]
most_commmon_words = counter.get_most_common_words(
df_j['content_cut'], top_n=20, min_frequency=5)
if len(most_commmon_words) > 0:
print('相关词汇：', most_commmon_words)
def cluster_content():
eps_var = Entry_Eps.get()
min_samples_var = Entry_MinSamples.get()
if eps_var == '' or min_samples_var == '':
messagebox.showinfo('Message', '请输全聚类参数！')
return
eps_var = float(eps_var)
min_samples_var = int(min_samples_var)
try:
df = news_pandas.load_news(os.path.join(temp_news_path,
'news_cut.csv'))
df['content_cut'] = df['content_cut'].map(eval)
df['content_'] = df['content_'].map(str)
except FileNotFoundError:
messagebox.showinfo('Message', '请先对新闻内容文本进行预处理！')
return
word_library_list = counter.get_word_library(df['content_cut'])
single_frequency_words_list = counter.get_single_frequency_words(
df['content_cut'])
max_features = len(
word_library_list) - len(single_frequency_words_list) // 2
matrix = modeling.feature_extraction(df['content_'],
vectorizer='TfidfVectorizer',
vec_args={
'max_df': 0.95,
'min_df': 1,
'max_features': max_features
})
dbscan = modeling.get_cluster(matrix,
cluster='DBSCAN',
cluster_args={
'eps': eps_var,
'min_samples': min_samples_var,
'metric': 'cosine'
})
labels = modeling.get_labels(dbscan)
df['label'] = labels
ranks = modeling.label2rank(labels)
df['rank'] = ranks
news_pandas.save_news(df, os.path.join(results_path, 'news_label.csv'))
df['matrix'] = matrix.toarray().tolist()
df_non_outliers = df[df['label'] != -1].copy()
if df_non_outliers.shape[0] == 0:
messagebox.showinfo('Message', '不能聚类出任何热点，请重新设置聚类参数！')
return
data_pca_tsne = modeling.feature_reduction(
df_non_outliers['matrix'].tolist(),
pca_n_components=3,
tsne_n_components=2)
df_non_outliers['pca_tsne'] = data_pca_tsne.tolist()
del df_non_outliers['matrix']
news_pandas.save_news(df_non_outliers,
os.path.join(results_path, 'news_non_outliers.csv'))
rank_num = counter.get_num_of_value_no_repeat(df_non_outliers['rank'])
hot_num.set(rank_num)
messagebox.showinfo('Message', '按照新闻内容聚类完成！')
def get_wordcloud(df, rank_column, text_list_column):
"""
按照不同的簇生成每个簇的词云
:param df: pd.DataFrame，带有排名和分词后的文本列表数据
:param rank_column: 排名列名
:param text_list_column: 分词后的文本列表列名
"""
df_non_outliers = modeling.get_non_outliers_data(df, label_column=rank_column)
label_num = counter.get_num_of_value_no_repeat(df_non_outliers[rank_column].tolist())
for i in range(1, label_num + 1):
df_ = df[df[rank_column] == i]
list_ = counter.flat(df_[text_list_column].tolist())
modeling.list2wordcloud(list_, save_path=os.path.join(results_path, rank_column, '%d.png' % i),
font_path=os.path.join(fonts_path, 'yw.ttf'))
