Massive changes to indexer and created merge
This commit is contained in:
inocturnis
parent
c4b3512df7
commit
53c7b49806
160
indexer.py
160
indexer.py
@ -34,162 +34,17 @@ from worker import Worker
|
|||||||
|
|
||||||
|
|
||||||
class Indexer():
|
class Indexer():
|
||||||
def __init__(self,restart,trimming):
|
def __init__(self,restart):
|
||||||
#Config stuffs
|
#Config stuffs
|
||||||
self.path = "data/DEV/"
|
self.path = "data/DEV/"
|
||||||
self.restart = restart
|
self.restart = restart
|
||||||
self.trimming = trimming
|
|
||||||
self.stemmer = PorterStemmer()
|
|
||||||
|
|
||||||
#Shelves for index
|
|
||||||
#https://www3.nd.edu/~busiforc/handouts/cryptography/letterfrequencies.html
|
|
||||||
#https://www.irishtimes.com/news/science/how-many-numbers-begin-with-a-1-more-than-30-per-cent-1.4162466
|
|
||||||
#According to this will be how we split things
|
|
||||||
#Save #1 = ABCD + (1) ~ 18.3% of words
|
|
||||||
#Save #2 = EFGHIJK + (2-3)~ 27.1% of words
|
|
||||||
#Save #3 = LMNOPQ + (4-7) ~ 25.4% of words
|
|
||||||
#Save #4 = RSTUVWXYZ + (8-9)~ 29.2% of words
|
|
||||||
#Save #5 = Special characters
|
|
||||||
if os.path.exists("save_1.shelve") and restart:
|
|
||||||
os.remove("save_1.shelve")
|
|
||||||
if os.path.exists("save_2.shelve") and restart:
|
|
||||||
os.remove("save_2.shelve")
|
|
||||||
if os.path.exists("save_3.shelve") and restart:
|
|
||||||
os.remove("save_3.shelve")
|
|
||||||
if os.path.exists("save_4.shelve") and restart:
|
|
||||||
os.remove("save_4.shelve")
|
|
||||||
if os.path.exists("save_5.shelve") and restart:
|
|
||||||
os.remove("save_5.shelve")
|
|
||||||
|
|
||||||
|
|
||||||
self.save_1 = shelve.open("save_1.shelve")
|
|
||||||
self.save_1_lock = threading.Lock()
|
|
||||||
self.save_2 = shelve.open("save_2.shelve")
|
|
||||||
self.save_2_lock = threading.Lock()
|
|
||||||
self.save_3 = shelve.open("save_3.shelve")
|
|
||||||
self.save_3_lock = threading.Lock()
|
|
||||||
self.save_4 = shelve.open("save_4.shelve")
|
|
||||||
self.save_4_lock = threading.Lock()
|
|
||||||
self.save_5 = shelve.open("save_5.shelve")
|
|
||||||
self.save_5_lock = threading.Lock()
|
|
||||||
|
|
||||||
print(len(list(self.save_1.keys())))
|
|
||||||
print(len(list(self.save_2.keys())))
|
|
||||||
print(len(list(self.save_3.keys())))
|
|
||||||
print(len(list(self.save_4.keys())))
|
|
||||||
print(len(list(self.save_5.keys())))
|
|
||||||
|
|
||||||
def save_index(self,word,posting):
|
|
||||||
cur_save = self.get_save_file(word)
|
|
||||||
lock = self.get_save_lock(word)
|
|
||||||
lock.acquire()
|
|
||||||
shelve_list = list()
|
|
||||||
try:
|
|
||||||
shelve_list = cur_save[word]
|
|
||||||
shelve_list.append(posting)
|
|
||||||
tic = perf_counter()
|
|
||||||
shelve_list.sort(key=lambda x: x.tf_idf, reverse = True)
|
|
||||||
toc = perf_counter()
|
|
||||||
if toc - tic > 1 :
|
|
||||||
print("Took " + str(toc - tic) + "seconds to sort shelve list !")
|
|
||||||
cur_save.sync()
|
|
||||||
lock.release()
|
|
||||||
except:
|
|
||||||
shelve_list.append(posting)
|
|
||||||
cur_save[word] = shelve_list
|
|
||||||
cur_save.sync()
|
|
||||||
lock.release()
|
|
||||||
|
|
||||||
def get_save_file(self,word):
|
|
||||||
#return the correct save depending on the starting letter of word
|
|
||||||
word_lower = word.lower()
|
|
||||||
|
|
||||||
if re.match(r"^[a-d0-1].*",word_lower):
|
|
||||||
return self.save_1
|
|
||||||
elif re.match(r"^[e-k2-3].*",word_lower):
|
|
||||||
return self.save_2
|
|
||||||
elif re.match(r"^[l-q4-7].*",word_lower):
|
|
||||||
return self.save_3
|
|
||||||
elif re.match(r"^[r-z8-9].*",word_lower):
|
|
||||||
return self.save_4
|
|
||||||
else:
|
|
||||||
print(word)
|
|
||||||
print("You have somehow went beyond the magic")
|
|
||||||
return self.save_5
|
|
||||||
|
|
||||||
def get_save_lock(self,word):
|
|
||||||
word_lower = word.lower()
|
|
||||||
if re.match(r"^[a-d0-1].*",word_lower):
|
|
||||||
return self.save_1_lock
|
|
||||||
elif re.match(r"^[e-k2-3].*",word_lower):
|
|
||||||
return self.save_2_lock
|
|
||||||
elif re.match(r"^[l-q4-7].*",word_lower):
|
|
||||||
return self.save_3_lock
|
|
||||||
elif re.match(r"^[r-z8-9].*",word_lower):
|
|
||||||
return self.save_4_lock
|
|
||||||
else:
|
|
||||||
print(word)
|
|
||||||
print("You have somehow went beyond the magic")
|
|
||||||
return self.save_5_lock.acquire()
|
|
||||||
|
|
||||||
# I have a test file (mytest.py) with pandas but couldn't figure out how to grab just a single cell.
|
|
||||||
# so I came up with this, if anyone knows how to get a single cell and can explain it to
|
|
||||||
# me I would love to know, as I think that method might be quicker, maybe, idk it like
|
|
||||||
# 4am
|
|
||||||
|
|
||||||
# retuns a dict of words/n-grams with their assosiated tf-idf score *can also return just a single score or a pandas dataframe
|
|
||||||
# https://stackoverflow.com/questions/34449127/sklearn-tfidf-transformer-how-to-get-tf-idf-values-of-given-words-in-documen
|
|
||||||
|
|
||||||
# Andy: added paramenter imporant_words in order to do multiplication of score
|
|
||||||
def get_tf_idf(self,words,word, important_words):
|
|
||||||
#tf_idf
|
|
||||||
#words = whole text
|
|
||||||
#word the word we finding the score for
|
|
||||||
#return the score
|
|
||||||
try:
|
|
||||||
tfidf = TfidfVectorizer()
|
|
||||||
tfidf_matrix = tfidf.fit_transform(words)
|
|
||||||
df = pd.DataFrame(tfidf_matrix.toarray(), columns = tfidf.get_feature_names_out())
|
|
||||||
score = df.iloc[0][''.join(word)]
|
|
||||||
for k,v in important_words.items():
|
|
||||||
if k == 'b' and word in v:
|
|
||||||
score = score * 1.2
|
|
||||||
elif k == 'h1' and word in v:
|
|
||||||
score = score * 1.75
|
|
||||||
elif k == 'h2' and word in v:
|
|
||||||
score = score * 1.5
|
|
||||||
elif k == 'h3' and word in v:
|
|
||||||
score = score * 1.2
|
|
||||||
elif k == 'title' and word in v:
|
|
||||||
score = score * 2
|
|
||||||
return(score)
|
|
||||||
#print(df)
|
|
||||||
except KeyError:
|
|
||||||
return -1
|
|
||||||
tfidf = TfidfVectorizer(ngram_range=(1,3)) # ngram_range is range of n-values for different n-grams to be extracted (1,3) gets unigrams, bigrams, trigrams
|
|
||||||
tfidf_matrix = tfidf.fit_transform(words) # fit trains the model, transform creates matrix
|
|
||||||
df = pd.DataFrame(tfidf_matrix.toarray(), columns = tfidf.get_feature_names_out()) # store value of matrix to associated word/n-gram
|
|
||||||
#return(df.iloc[0][''.join(word)]) #used for finding single word in dataset
|
|
||||||
data = df.to_dict() # transform dataframe to dict *could be expensive the larger the data gets, tested on ~1000 word doc and took 0.002 secs to run
|
|
||||||
return data # returns the dict of words/n-grams with tf-idf
|
|
||||||
#print(df) # debugging
|
|
||||||
except:
|
|
||||||
print("Error in tf_idf!")
|
|
||||||
return
|
|
||||||
|
|
||||||
|
|
||||||
def get_data(self):
|
def get_data(self):
|
||||||
|
|
||||||
num_threads = 1
|
num_threads = 1
|
||||||
threads = list()
|
threads = list()
|
||||||
|
|
||||||
for directory in os.listdir(self.path):
|
for directory in os.listdir(self.path):
|
||||||
for file in os.listdir(self.path + "/" + directory + "/"):
|
for file in os.listdir(self.path + "/" + directory + "/"):
|
||||||
#Actual files here
|
|
||||||
#JSON["url"] = url of crawled page, ignore fragments
|
|
||||||
#JSON["content"] = actual HTML
|
|
||||||
#JSON["encoding"] = ENCODING
|
|
||||||
index = 0
|
|
||||||
while True:
|
while True:
|
||||||
file_path = self.path + "" + directory + "/"+file
|
file_path = self.path + "" + directory + "/"+file
|
||||||
if len(threads) < num_threads:
|
if len(threads) < num_threads:
|
||||||
@ -212,17 +67,6 @@ class Indexer():
|
|||||||
#
|
#
|
||||||
|
|
||||||
#getting important tokens
|
#getting important tokens
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
def main():
|
||||||
indexer = Indexer(True,0)
|
indexer = Indexer(True,0)
|
||||||
|
|||||||
@ -1,10 +1,12 @@
|
|||||||
#Posting class for indexer, will probably be more complex as we keep adding crap to it
|
#Posting class for indexer, will probably be more complex as we keep adding crap to it
|
||||||
|
|
||||||
class Posting():
|
class Posting():
|
||||||
def __init__(self,url,tf_idf):
|
def __init__(self,doc_id,tf_raw,tf_idf,positionals):
|
||||||
self.url = url
|
self.doc_id = doc_id
|
||||||
|
self.tf_raw = tf_raw
|
||||||
self.tf_idf = tf_idf
|
self.tf_idf = tf_idf
|
||||||
|
self.positionals = positionals
|
||||||
|
|
||||||
def comparator(self):
|
def comparator(self):
|
||||||
#Some custom comparator for sorting postings later
|
#Some custom comparator for sorting postings later
|
||||||
pass
|
pass
|
||||||
128
test.py
128
test.py
@ -1,17 +1,115 @@
|
|||||||
import re
|
import json
|
||||||
import os
|
from posting import Posting
|
||||||
|
import math
|
||||||
|
import sys
|
||||||
|
import random
|
||||||
|
from nltk.corpus import words
|
||||||
|
random_list = [1,2,3,4,5,6,7,8,9,10]
|
||||||
|
|
||||||
for i in range(99):
|
|
||||||
word_lower = chr(i % 26 + 97) + chr(i % 26 + 97 + 1)
|
|
||||||
print(word_lower)
|
|
||||||
if re.match(r"^[a-d1-1].*",word_lower):
|
|
||||||
print("SAVE 1")
|
|
||||||
elif re.match(r"^[e-k2-3].*",word_lower):
|
|
||||||
print("SAVE 2")
|
|
||||||
elif re.match(r"^[l-q4-7].*",word_lower):
|
|
||||||
print("SAVE 3")
|
|
||||||
elif re.match(r"^[r-z8-9].*",word_lower):
|
|
||||||
print("SAVE 4")
|
|
||||||
|
|
||||||
path = "data/DEV/"
|
test_data = words.words()
|
||||||
print(os.listdir(path))
|
random.shuffle(test_data)
|
||||||
|
|
||||||
|
class Node():
|
||||||
|
index_value = ''
|
||||||
|
postings = list()
|
||||||
|
|
||||||
|
class Index():
|
||||||
|
length = 0
|
||||||
|
index = list()
|
||||||
|
|
||||||
|
def random_posting(id):
|
||||||
|
return Posting(id,random.choice(random_list),random.choice(random_list),[random.choice(random_list),random.choice(random_list),random.choice(random_list),random.choice(random_list),
|
||||||
|
random.choice(random_list),random.choice(random_list),random.choice(random_list),random.choice(random_list)])
|
||||||
|
|
||||||
|
def random_partial_index(name):
|
||||||
|
part_index = Index()
|
||||||
|
part_index.index = list()
|
||||||
|
part_index.length = 0
|
||||||
|
with open(name +'.partial', 'w') as f:
|
||||||
|
for i in range(1000):
|
||||||
|
|
||||||
|
node1 = Node()
|
||||||
|
node1.index_value = random.choice(test_data).lower()
|
||||||
|
node1.postings = list()
|
||||||
|
for i in range(10):
|
||||||
|
node1.postings.append(random_posting(i))
|
||||||
|
|
||||||
|
jsonStr = json.dumps(node1, default=lambda o: o.__dict__,sort_keys=False)
|
||||||
|
|
||||||
|
part_index.index.append((node1.index_value,f.tell()))
|
||||||
|
f.write(jsonStr + '\n')
|
||||||
|
part_index.length = part_index.length + 1
|
||||||
|
|
||||||
|
part_index.index.sort(key=lambda y:y[0])
|
||||||
|
jsonStr =json.dumps(part_index, default=lambda o: o.__dict__,sort_keys=False)
|
||||||
|
with open(name + '.index','w') as f:
|
||||||
|
f.write(jsonStr)
|
||||||
|
|
||||||
|
def merge(partial_indices):
|
||||||
|
partial_files = list()
|
||||||
|
partial_index_files = list()
|
||||||
|
parital_index_indices = list()
|
||||||
|
merged_index = open("merged_index.full",'w')
|
||||||
|
num_indices = len(partial_indices)
|
||||||
|
|
||||||
|
#Full Index.Index and Length
|
||||||
|
full_index = Index()
|
||||||
|
full_index.index = list()
|
||||||
|
full_index.length = 0
|
||||||
|
|
||||||
|
for partial_index in partial_indices:
|
||||||
|
file = open(partial_index+'.partial','r')
|
||||||
|
partial_files.append(file)
|
||||||
|
index = open(partial_index+'.index','r')
|
||||||
|
partial_index_files.append(index)
|
||||||
|
|
||||||
|
for partial_index_file in partial_index_files:
|
||||||
|
partial_index_file.seek(0,0)
|
||||||
|
parital_index_indices.append(json.loads(partial_index_file.readline()))
|
||||||
|
|
||||||
|
#Start all indexes at 0
|
||||||
|
for partial_file in partial_files:
|
||||||
|
partial_file.seek(0,0)
|
||||||
|
|
||||||
|
pointers = [0]*num_indices
|
||||||
|
|
||||||
|
while(True):
|
||||||
|
|
||||||
|
#Get all values from all indices to find min
|
||||||
|
value = None
|
||||||
|
values = list()
|
||||||
|
for i in range(num_indices):
|
||||||
|
if pointers[i] < parital_index_indices[i]['length']:
|
||||||
|
values.append(parital_index_indices[i]['index'][pointers[i]][0])
|
||||||
|
|
||||||
|
if(len(values) == 0):
|
||||||
|
break
|
||||||
|
value = min(values)
|
||||||
|
|
||||||
|
#Get data from the min value of all indices if exists then save to mergedIndex
|
||||||
|
if value == None:
|
||||||
|
print("I have crashed some how by not getting min value")
|
||||||
|
break
|
||||||
|
|
||||||
|
node = Node()
|
||||||
|
node.index_value = value
|
||||||
|
for i in range(num_indices):
|
||||||
|
if pointers[i] < parital_index_indices[i]['length'] and parital_index_indices[i]['index'][pointers[i]][0] == value:
|
||||||
|
to_seek = parital_index_indices[i]['index'][pointers[i]][1]
|
||||||
|
partial_files[i].seek(to_seek,0)
|
||||||
|
json_value = partial_files[i].readline()
|
||||||
|
temp_node = json.loads(json_value)
|
||||||
|
node.postings = node.postings + temp_node['postings']
|
||||||
|
pointers[i] = pointers[i] + 1
|
||||||
|
|
||||||
|
node.postings.sort(key=lambda y:y['doc_id'])
|
||||||
|
full_index.index.append((value,merged_index.tell()))
|
||||||
|
full_index.length = full_index.length + 1
|
||||||
|
jsonStr = json.dumps(node,default=lambda o: o.__dict__,sort_keys=False)
|
||||||
|
merged_index.write(jsonStr + '\n')
|
||||||
|
|
||||||
|
full_index.index.sort(key=lambda y:y[0])
|
||||||
|
jsonStr =json.dumps(full_index, default=lambda o: o.__dict__,sort_keys=False)
|
||||||
|
with open("merged_index.index" ,'w') as f:
|
||||||
|
f.write(jsonStr)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user