description of file

Author: ayushianan

lib/__pycache__/querying.cpython-36.pyc

@@ -3,6 +3,21 @@
 import sys
 from nltk.corpus import stopwords
 import re
+import sys, os
+projectpath = os.path.dirname(os.path.realpath('querying.py'))
+#directory path
+libpath = projectpath + '/lib'
+#lib path
+sys.path.append(libpath)
+os.chdir(projectpath)
+import parsing
+import re
+import time
+collection = 'New Testament'
+#mongo folder
+# Indicate the path where relative to the collection
+os.chdir(projectpath + '/data/' + collection)
+files = [file for file in os.listdir('.') if os.path.isfile(file)]
 
 def cleanQuery(string):
     frenchStopWords = stopwords.words('french')
@@ -33,4 +48,29 @@ def rankDocuments(index, words):
     rankings = list(reversed(sorted(rankings.items(), key=lambda x: x[1])))
     return rankings
 
+def rankDocuments1(index, words):
+    # We rank each document based on query
+    rankings = {}
+    for word in words:
+        for document in index[word]['document(s)'].keys():
+            # Term Frequency (log to reduce document size scale effect)
+            TF = index[word]['document(s)'][document]['position(s)']
+            for file in files:
+                        name = re.match('(^[^.]*)', file).group(0)
+                        if name==document:
+                                    data = open(file).read().splitlines()   
+                                    words = parsing.clean(data)     
+            # Store scores in the ranking dictionary
+            if document not in rankings:
+                rankings[document] = words[TF[0]-10:TF[0]+10]
+            else:
+                rankings[document] += words[TF[0]-10:TF[0]+10]
+            #print(rankings[document])
+            #print(document)
+            #print("11111111111111")
+    # Order results according to the scores
+    rankings = list(rankings.items())
+    #print(rankings[0])
+    return rankings
+
 

lib/querying.py

@@ -1,13 +1,23 @@
 import sys, os
+import itertools
 projectpath = os.path.dirname(os.path.realpath('main.py'))
 libpath = projectpath + '/lib'
 sys.path.append(libpath)
 os.chdir(projectpath)
 from PyQt4 import QtCore, QtGui
 from browser import Ui_MainWindow
-from querying import cleanQuery, rankDocuments
+from querying import cleanQuery, rankDocuments,rankDocuments1
 from pymongo import MongoClient
 
+import parsing
+import re
+import time
+collection = 'New Testament'
+#mongo folder
+# Indicate the path where relative to the collection
+os.chdir(projectpath + '/data/' + collection)
+files = [file for file in os.listdir('.') if os.path.isfile(file)]
+
 # Connect to the database containing inverted indexes
 client = MongoClient()
 db = client.Inverted_Index
@@ -17,30 +27,56 @@
 
 
 class browser(QtGui.QMainWindow):
-    def __init__(self, parent = None):
-        QtGui.QWidget.__init__(self, parent)
-        self.ui = Ui_MainWindow()
-        self.ui.setupUi(self)
+	def __init__(self, parent = None):
+		QtGui.QWidget.__init__(self, parent)
+		self.ui = Ui_MainWindow()
+		self.ui.setupUi(self)
         # Connect the query function with the search button
-        self.ui.pushButton.clicked.connect(self.query)
-    def query(self):
+		self.ui.pushButton.clicked.connect(self.query)
+	def query(self):
         # Empty the list
-        self.ui.listWidget.clear()
+		self.ui.listWidget.clear()
         # Get the words in the query
-        words = cleanQuery(self.ui.lineEdit.text())
+		words = cleanQuery(self.ui.lineEdit.text())
         # Collect the information for each word of the query
-        index = {}
-        for word in words:
-            index[word] = collection.find({'_id' : word})[0]['info']
+		index = {}
+		for word in words:
+			index[word] = collection.find({'_id' : word})[0]['info']
         # Rank the documents according to the query
-        results = rankDocuments(index, words)
-        i=0
-        for result in results:
-            if(i<10):
-                self.ui.listWidget.addItem(result[0]+' : '+str(round(result[1], 2)))
-            i=i+1
-	    
-	    
+		results = rankDocuments(index, words)
+		results1 = rankDocuments1(index, words)
+		size=len(results)
+		print(size)
+		#print(results1[0])
+        #results1 = rankDocuments1(index, words)
+		i=0
+		rankings = {}
+		for result in results:
+			if(i<10):
+				self.ui.listWidget.addItem(result[0]+' : '+str(round(result[1], 2)))
+				j=0
+				for j in range(size):
+					if(result[0]==results1[j][0]):
+						break
+				self.ui.listWidget.addItem(str(" ".join(results1[j][1])))
+				'''for word in words:
+					for document in index[word]['document(s)'].keys():
+    						# Term Frequency (log to reduce document size scale effect)
+						TF = index[word]['document(s)'][document]['position(s)']
+						for file in files:
+							name = re.match('(^[^.]*)', file).group(0)
+							if name==document:
+								data = open(file).read().splitlines()   
+								words = parsing.clean(data)     
+            				# Store scores in the ranking dictionary
+						if document not in rankings:
+							rankings[document] = words[TF[0]-10:TF[0]+10]
+						else:
+							rankings[document] += words[TF[0]-10:TF[0]+10]
+						#self.ui.listWidget.addItem(rankings[document])
+						print(rankings[document])'''
+			i=i+1
+
 
 if __name__ == "__main__":
     app = QtGui.QApplication(sys.argv)