finished datacollection

spacetime-crawler4py-master/datacollection.py

@@ -0,0 +1,114 @@
+import re
+import urllib.request
+from urllib.parse import urlparse
+from urllib.parse import urljoin
+from bs4 import BeautifulSoup
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.corpus import words
+import html2text
+import nltk
+# nltk.download('stopwords')
+# nltk.download('words')
+# there is another nltk.download() requirement but I removed it so i forgot what it was
+#       it'll show in the console/terminal if you run the code i believe
+#       it showed in mine
+
+# To explain this class I have to start by explaining the container I decided on using to keep track of subdomains of ics.uci.edu
+# I decided to use a dict. Long story short, I was trying to figure out what to make my key so it would uniquely identify what I needed it to do.
+#       I was going to use the parsed.netloc; however, we're taking into account that a link that looks like https://somename.vision.ics.uci.edu
+#       is a unique link of the subdomain vision.
+# And so I made the key the subdomain that is before ics.uci.edu in the link, and the value of the dict is this class
+#       It's a very simple class, so I'm not going to commenting what it does
+class urlData:
+    def __init__(self, url, subdomain, domain):
+        self.url = url
+        self.nicelink = "http://" + removeFragment(url).netloc
+        self.domain = domain
+        self.subdomain = subdomain
+        self.uniques = set()
+        self.uniques.add(removeFragment(url))
+    
+    def getDomain(self):
+        return self.domain
+    
+    def getURL(self):
+        return self.url
+
+    def getNiceLink(self):
+        return self.nicelink
+
+    def getSub(self):
+        return self.subdomain
+    
+    def getUniques(self):
+        return self.uniques
+
+    def appendUnique(self, parse):
+        self.uniques.add(parse)
+
+# Tried to find a libary that would do this for me, but couldn't
+# It parses the url and uses the netloc to separat for domain and subdomain
+def findDomains(url):
+    urlsplit = url.split('.')
+    if urlsplit[0].lower() == 'www':
+        urlsplit.remove('www')
+        for i in range(len(urlsplit)):
+            if urlsplit[i] == 'ics':
+                if i == 0:
+                    return 0, 0
+                elif i == 1:
+                    return urlsplit[0], urlsplit[1]
+                else:
+                    return urlsplit[i-1], urlsplit[i] #something like random.vision.ics.uci.edu will be consider a unique page of vision
+        return None, None
+    else:
+        for i in range(len(urlsplit)):
+            if urlsplit[i] == 'ics':
+                if i == 0:
+                    return 0, 0
+                elif i == 1:
+                    return urlsplit[0], urlsplit[1]
+                else:
+                    return urlsplit[i-1], urlsplit[i] #something like random.vision.ics.uci.edu will be consider a unique page of vision
+        return None, None
+
+def tokenize(url):
+    # getting connection from url
+    page = urllib.request.urlopen(url)
+    data = page.read()
+
+    # named it tSoup for merge convience
+    # need the 'lxml' parser for this.
+    #       When extract_next_links is called it returns a list full of links with no resp, and I had to find a way to get text from just link.
+    #       Therefore, I decided to get the plain text this way.
+    tSoup = BeautifulSoup(data, 'lxml')
+
+    # Floyd (1 March 2021) Stackoverflow. https://stackoverflow.com/questions/328356/extracting-text-from-html-file-using-python
+    #       compared this with tSoup.get_text() and clean_text just provided content easier to tokenize and more inline with my intentions
+    clean_text = ' '.join(tSoup.stripped_strings)
+    token = word_tokenize(clean_text)
+
+    # This used the nltk.corpus and just removes the tokens that aren't words
+    token = [i for i in token if i.lower() in words.words()]
+
+    return token
+
+#added this so the scraper code is not too redundant
+def computeFrequencies(tokens, d):
+    for t in tokens:
+        if t not in d:
+            d[t] = 1
+        else:
+            d[t] += 1
+
+def removeStopWords(toks):
+    stopWords = set(stopwords.words('english'))
+    return [t for t in toks if t.lower() if not t.lower() in stopWords]
+    
+def removeFragment(u):
+    # turn into a urlparse object
+    # removed fragment in order to have "unique" links
+    removefrag = urlparse(u)
+    removefrag = removefrag._replace(fragment = '')
+    return removefrag

spacetime-crawler4py-master/scraper.py

@@ -1,34 +1,115 @@
 import re
+import urllib.request
 from urllib.parse import urlparse
 from urllib.parse import urljoin
 from bs4 import BeautifulSoup
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.corpus import words
+import html2text
+import nltk
+#moved all my code to a separted py file and imported it here
+from datacollection import *
+
+# nltk.download('stopwords')
+# nltk.download('words')
+# there is another nltk.download() requirement but I removed it so i forgot what it was
+#       it'll show in the console/terminal if you run the code i believe. it appeared in mine
 
 def scraper(url, resp):
-    #initialize set for unique links
-    #used a set for elimatining duplicates
+    # initialize set for unique links
+    #       used a set for elimatining duplicates
     uniques = set() 
+    # have to add the original url to the unique set
+    copyoriginal = url
+    uniques.add(removeFragment(copyoriginal))
+
+    # initializing longest for finding the longest page
+    max = -9999
+    longest = None
+
+    # have to do this for the original url
+    tok = tokenize(url)
+    if len(tok) > max:
+        max = len(tok)
+        longest = url
+
+    # grand_dict is a dictionary that contains every word over the entire set of pages (excluding stop words)
+    #       key: word , value: frequencies
+    grand_dict = dict()
+    tok = removeStopWords(tok)
+    computeFrequencies(tok, grand_dict)
+
+    # ics is a dict with subdomains
+    ics = dict()
+    
 
     links = extract_next_links(url, resp)
     links_valid = list()
     valid_links = open("valid_links.txt",'a')
     invalid_links = open("invalid_links.txt",'a')
+
+    
     for link in links:
         if is_valid(link):
             links_valid.append(link)
             valid_links.write(link + "\n")
 
-            #turn into a urlparse object
-            #removed fragment in order to have "unique" links
-            remove_frag = urlparse(url)
-            remove_frag = remove_frag._replace(fragment = '')
-            uniques.add(remove_frag) 
+            # Answering q1 for report
+            uniques.add(removeFragment(link)) 
+
+            # Answering q2
+            tempTok = tokenize(link)
+            if len(tempTok) > max:
+                max = len(tempTok)
+                longest = link
+
+
+            # Answering q3
+            tempTok = removeStopWords(tempTok)
+            computeFrequencies(tempTok, grand_dict)
+
+            # Answering q4 
+            fragless = removeFragment(link)
+            domain = findDomains(fragless.netloc)
+            if domain[1] == 'ics':
+                if domain[0] not in ics:
+                    ics[domain[0]] = urlData(link, domain[0], domain[1])
+                else:
+                    if fragless not in ics[domain[0]].getUniques():
+                        ics[domain[0]].appendUnique(fragless)
+                    
+
         else:
             invalid_links.write("From: " + url + "\n")
             invalid_links.write(link + "\n")
 
-    #creating text file that includes the number of unique links
-    f = open("numUniqueLinks.txt", "w")
-    f.write("{length}".format(length = len(uniques)))
+    # creating text file that includes the number of unique links
+    f = open("q1.txt", "w")
+    f.write("Number of unique pages: {length}".format(length = len(uniques)))
+    f.close()
+
+    # creating text file for question 2
+    f = open("q2.txt", "w")
+    f.write("Largest page url: {url} \nLength of page: {length}".format(url = longest, length = max))
+    f.close()
+
+    # creating text file for question 3
+    f = open("q3.txt", "w")
+    sortedGrandDict = {k: v for k, v in sorted(grand_dict.items(), key=lambda item: item[1], reverse = True)}
+    i = 0
+    for k, v in sortedGrandDict.items():
+        if i == 50:
+            break
+        else:
+            f.write(k, ':', v)
+    f.close()
+
+    # creating text file for question 4
+    sortedDictKeys = sorted(ics.keys())
+    f = open("q4.txt", "w")
+    for i in sortedDictKeys:
+        f.write("{url}, {num}".format(url = ics[i].getNiceLink(), num = len(ics[i].getUniques())))
     f.close()
 
     return links_valid