<commit_before>__author__ = '<NAME>'

from invdx import build_data_structures
from rank import *
import operator


class QueryProcessor:
	def __init__(self, queries, corpus, score_function='BM25'):
		self.queries = queries
		self.index, self.ft, self.dlt = build_data_structures(corpus)
		self.score_function = score_function

	def run(self):
		results = []
		qid = 0
		for query in self.queries:
			if self.score_function == 'BM25':
				results.append(self.run_BM25(query))
			elif self.score_function == 'Query Likelihood':
				print 'running query %d' % qid
				qid += 1
				results.append(self.run_QueryLikelihood(query))
		return results

	def run_BM25(self, query):
		query_result = dict()
		for term in query:
			if term in self.index:
				doc_dict = self.index[term] # retrieve index entry
				for docid, freq in doc_dict.iteritems(): #for each document and its word frequency
					score = score_BM25(n=len(doc_dict), f=freq, qf=1, r=0, N=len(self.dlt),
									   dl=self.dlt.get_length(docid),
									   avdl=self.dlt.get_average_length()) # calculate score
					if docid in query_result: #this document has already been scored once
						query_result[docid] += score
					else:
						query_result[docid] = score
		return query_result

	def run_QueryLikelihood(self, query):
		query_result = dict()
		for mu in mu_values:
			mu_result = dict()
			for term in query:
				if term in self.index:
					for docid, freq in self.index[term].iteritems():
						score = score_query_likelihood(f=freq, mu=mu, c=self.ft.get_frequency(term),
													   C=len(self.index), D=len(self.dlt))
						if docid in mu_result:
							mu_result[docid] += score<commit_msg>QueryProcessor now uses OrderedDict to avoid parsing for mu.<commit_after>__author__ = '<NAME>'


from invdx import build_data_structures
from rank import *
from collections import OrderedDict
import operator


class QueryProcessor:
	def __init__(self, queries, corpus, score_function='BM25'):
		self.queries = queries
		self.index, self.ft, self.dlt = build_data_structures(corpus)
		self.score_function = score_function

	def run(self):
		results = []
		qid = 0
		for query in self.queries:
			if self.score_function == 'BM25':
				results.append(self.run_BM25(query))
			elif self.score_function == 'Query Likelihood':
				print 'running query %d' % qid
				qid += 1
				results.append(self.run_QueryLikelihood(query))
		return results

	def run_BM25(self, query):
		query_result = dict()
		for term in query:
			if term in self.index:
				doc_dict = self.index[term] # retrieve index entry
				for docid, freq in doc_dict.iteritems(): #for each document and its word frequency
					score = score_BM25(n=len(doc_dict), f=freq, qf=1, r=0, N=len(self.dlt),
									   dl=self.dlt.get_length(docid),
									   avdl=self.dlt.get_average_length()) # calculate score
					if docid in query_result: #this document has already been scored once
						query_result[docid] += score
					else:
						query_result[docid] = score
		return query_result

	def run_QueryLikelihood(self, query):
		query_result = OrderedDict()
		for mu in mu_values:
			mu_result = dict()
			for term in query:
				if term in self.index:
					for docid, freq in self.index[term].iteritems():
						score = score_query_likelihood(f=freq, mu=mu, c=self.ft.get_frequency(term),
													   C=len(self.index), D=len(self.dlt))
						if docid in mu_result:
							mu_result[docid] += score
{'fraction_non_alphanumeric': 0.10533994747592647, 'fraction_numerical': 0.008170411438576015, 'mean_word_length': 3.613728129205922, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1399528', 'n_tokens_mistral': 1397, 'n_tokens_neox': 1132, 'n_words': 305}