def __init__(self, freq_data, selected_docs, context_doc_groups, global_scores):
print 'start query'
self.selected_docs = selected_docs
self.phrase_cnt = agg_phrase_cnt(freq_data, selected_docs)
self.phrase_df = agg_phrase_df(freq_data, selected_docs)
self.phrase_cnt_context = {}
self.phrase_df_context = {}
if len(self.phrase_df) > 0:
self.max_df = max(self.phrase_df.values())
else:
self.max_df = 0
self.max_df_context = {}
self.dc_context = {}
self.self_dc = len(selected_docs)
self.sum_cnt = sum(self.phrase_cnt.values())
self.sum_cnt_context = {}
self.global_scores = global_scores
for group, docs in context_doc_groups.items():
self.phrase_cnt_context[group] = agg_phrase_cnt(freq_data, docs)
self.phrase_df_context[group] = agg_phrase_df(freq_data, docs)
if len(self.phrase_df_context[group]) > 0:
self.max_df_context[group] = max(self.phrase_df_context[group].values())
else:
self.max_df_context[group] = 0
self.dc_context[group] = len(docs)
self.sum_cnt_context[group] = sum(self.phrase_cnt_context[group].values())
# added for exploration
self.context_groups = {}
self.ranked_list = []
def update_selected_docs(self, freq_data, selected_docs, phrases=[]): if not phrases: self.selected_docs = selected_docs self.phrase_cnt = agg_phrase_cnt(freq_data, selected_docs) self.phrase_df = agg_phrase_df(freq_data, selected_docs) if len(self.phrase_df) > 0: self.max_df = max(self.phrase_df.values()) else: self.max_df = 0 self.self_dc = len(selected_docs) self.sum_cnt = sum(self.phrase_cnt.values()) self.ranked_list = []
