ExampleNotebooks/.ipynb_checkpoints/Web2Warc-checkpoint.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ExampleNotebooks/Web2Warc.ipynb

+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

ExampleNotebooks/dataExtract.ipynb

+%% Cell type:code id: tags:
+
+``` python
+import os
+import pandas as pd
+from html_similarity import style_similarity, structural_similarity, similarity
+from bs4 import BeautifulSoup, Doctype
+from bs4.element import Comment
+from collections import Counter
+from scipy.spatial import distance
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.tokenize.treebank import TreebankWordDetokenizer
+import string
+import spacy
+from nltk.metrics import edit_distance
+from nltk.metrics import edit_distance
+from nltk.metrics import interval_distance
+from nltk import jaccard_distance
+import textdistance
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import datetime
+import fbprophet
+import gc
+from fastparquet import ParquetFile
+import pyarrow.parquet as pq
+import json
+from pyspark.sql import SparkSession
+from pyspark.sql import SQLContext
+```
+
+%% Output
+
+    ERROR:fbprophet:Importing plotly failed. Interactive plots will not work.
+
+%% Cell type:code id: tags:
+
+``` python
+root = '../data'
+modelUrl = 'cnn.com'
+
+parquetFiles = []
+for root, dirs, files in os.walk(root):
+    path = root.split(os.sep)
+    for file in files:
+        if file.endswith(".parquet"):
+            parquetFiles.append(os.path.join(root, file))
+
+print(str(len(parquetFiles)) + " parquet files found")
+```
+
+%% Output
+
+    178 parquet files found
+
+%% Cell type:code id: tags:
+
+``` python
+spark = SparkSession.builder \
+    .master("local[*]")\
+    .config("spark.executor.memory", "70g")\
+    .config("spark.driver.memory", "50g")\
+    .config("spark.memory.offHeap.enabled", "true")\
+    .config("spark.memory.offHeap.size", "14g")\
+    .appName("sampleCodeForReference")\
+    .config("spark.driver.cores", "12")\
+    .getOrCreate()
+
+spark.conf.set("spark.sql.parquet.enableVectorizedReader","false")
+
+sc = spark.sparkContext
+
+sqlContext = SQLContext(sc)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+archiveData = []
+```
+
+%% Cell type:code id: tags:
+
+``` python
+for k in range(len(parquetFiles)):
+
+    #print("Processing File " + str(k+1))
+
+    try:
+        file = sqlContext.read.parquet(parquetFiles[k])
+
+        UriComponents = file.rdd.take(1)[0].originalUrl.split('/')
+        payload = file.rdd.take(1)[0].payload
+        mime = file.rdd.take(1)[0].mime
+        filename = file.rdd.take(1)[0].filename
+        timestamp = filename.split('.')[0][4:]
+
+        #print(mime, UriComponents, len(payload))
+        print("Processing File " + str(k+1))
+
+        if (mime == 'text/html' and len(payload) > 1 and modelUrl in UriComponents[-1]):
+            currentData = {}
+            currentData['payload'] = payload
+            currentData['timestamp'] = timestamp
+            archiveData.append(currentData)
+
+    except:
+        pass
+
+
+
+```
+
+%% Output
+
+    Processing File 1
+    Processing File 2
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+
+%% Cell type:code id: tags:
+
+``` python
+archiveData.sort(key=lambda x: x['timestamp'], reverse=False)
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df = pd.DataFrame(archiveData, columns = ['payload', 'timestamp'])
+```
+
+%% Cell type:code id: tags:
+
+``` python
+df.to_pickle("./archiveData.pkl")
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```
+
+%% Cell type:code id: tags:
+
+``` python
+```

archiveTextClassifier.py

+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import os
+import pandas as pd
+from html_similarity import style_similarity, structural_similarity, similarity
+from bs4 import BeautifulSoup, Doctype
+from bs4.element import Comment
+from collections import Counter
+from scipy.spatial import distance
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize
+from nltk.tokenize.treebank import TreebankWordDetokenizer
+import string
+import spacy
+from nltk.metrics import edit_distance
+from nltk.metrics import edit_distance
+from nltk.metrics import interval_distance
+from nltk import jaccard_distance
+import textdistance
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn import svm
+
+
+# In[3]:
+
+
+def tag_visible(element):
+    if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:
+        return False
+    if isinstance(element, Comment):
+        return False
+    return True
+
+
+# In[4]:
+
+
+def text_from_html(htmlPage):
+    soup = BeautifulSoup(htmlPage, 'html.parser')
+    texts = soup.findAll(text=True)
+    visible_texts = filter(tag_visible, texts)
+    return u" ".join(t.strip() for t in visible_texts)
+
+
+# In[5]:
+
+
+def split(word): 
+    return [char for char in word]
+
+
+# In[6]:
+
+
+def filter_text(text):
+    stop_words = set(stopwords.words('english'))
+    stop_words.update(split(string.punctuation))
+    nlp = spacy.load('en_core_web_sm')
+    spacy_stopwords = spacy.lang.en.stop_words.STOP_WORDS
+    stop_words.update(spacy_stopwords)
+    #stop_words.update(["\\t","\\n","\\r"])
+    
+    text = text.replace("\\n", "")
+    text = text.replace("\\r", "")
+    text = text.replace("\\t", "")    
+    
+    word_tokens_text = word_tokenize(text)
+
+    filtered_text = [w for w in word_tokens_text if not w in stop_words]
+
+    filtered_text = TreebankWordDetokenizer().detokenize(filtered_text)
+    
+    return filtered_text
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[7]:
+
+
+def classiyRF(archiveData, newRecord):
+    archiveData.sort(key=lambda x: x['timestamp'], reverse=False)
+    
+    basePayload = archiveData[0]['payload']
+    basePayloadText = text_from_html(basePayload)
+    basePayloadFilteredText = filter_text(basePayloadText)
+    lastSavedDataIndex = 0
+    dataset = []
+
+    print(str(len(archiveData)) + " datapoints found")
+
+    for i in range(1, len(archiveData)):
+        if(i % 100 is 0):
+            print(str(i) + " Records processed")
+
+        hasContentChanged = False
+
+        overallSimilarity = similarity(basePayload, archiveData[i]['payload'])
+        styleSimilarity = style_similarity(basePayload, archiveData[i]['payload'])
+        structuralSimilarity = structural_similarity(basePayload, archiveData[i]['payload'])
+
+        archiveText = text_from_html(archiveData[i]['payload'])
+        filteredArchiveText = filter_text(archiveText)
+
+        cosineSimilarity = textdistance.cosine.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+        jaccardSimilarity = textdistance.jaccard.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+        #editDistanceSimilarity = textdistance.levenshtein.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+        sorensenDiceSimilarity = textdistance.sorensen_dice.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+
+        if(overallSimilarity < 0.80 or cosineSimilarity < 0.95):
+            hasContentChanged = True
+            lastSavedDataIndex = i
+            basePayload = archiveData[i]['payload']
+            basePayloadText = archiveText
+            basePayloadFilteredText = filteredArchiveText
+
+        data = [overallSimilarity, styleSimilarity, structuralSimilarity,                cosineSimilarity, jaccardSimilarity, sorensenDiceSimilarity, hasContentChanged]
+        dataset.append(data)
+
+    
+    
+    df = pd.DataFrame(dataset, columns = ['similarity', 'styleSimilarity', 'structureSimilarity',                                           'cosine', 'jaccard', 'sorensen', 'changed']) 
+    print("Dataframe created")
+    
+    X = df.iloc[:, 0:6].values
+    y = df.iloc[:, 6].values
+    
+    sc = StandardScaler()
+    X_train = sc.fit_transform(X)
+    
+    
+    overallSimilarity = similarity(basePayload, newRecord['payload'])
+    styleSimilarity = style_similarity(basePayload, newRecord['payload'])
+    structuralSimilarity = structural_similarity(basePayload, newRecord['payload'])
+
+    archiveText = text_from_html(newRecord['payload'])
+    filteredArchiveText = filter_text(archiveText)
+
+    cosineSimilarity = textdistance.cosine.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    jaccardSimilarity = textdistance.jaccard.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    #editDistanceSimilarity = textdistance.levenshtein.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    sorensenDiceSimilarity = textdistance.sorensen_dice.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    
+    X_test = [overallSimilarity, styleSimilarity, structuralSimilarity,                cosineSimilarity, jaccardSimilarity, sorensenDiceSimilarity]
+    
+    
+    print("Starting Random Forest Classification")
+    
+    regressor = RandomForestClassifier(n_estimators=20, random_state=0)
+    regressor.fit(X_train, y)
+    y_pred = regressor.predict([X_test])
+    
+    return y_pred
+
+    
+    
+    
+    
+    
+
+
+# In[ ]:
+
+
+def classiySVM(archiveData, newRecord):
+    archiveData.sort(key=lambda x: x['timestamp'], reverse=False)
+    
+    basePayload = archiveData[0]['payload']
+    basePayloadText = text_from_html(basePayload)
+    basePayloadFilteredText = filter_text(basePayloadText)
+    lastSavedDataIndex = 0
+    dataset = []
+
+    print(str(len(archiveData)) + " datapoints found")
+
+    for i in range(1, len(archiveData)):
+        if(i % 100 is 0):
+            print(str(i) + " Records processed")
+
+        hasContentChanged = False
+
+        overallSimilarity = similarity(basePayload, archiveData[i]['payload'])
+        styleSimilarity = style_similarity(basePayload, archiveData[i]['payload'])
+        structuralSimilarity = structural_similarity(basePayload, archiveData[i]['payload'])
+
+        archiveText = text_from_html(archiveData[i]['payload'])
+        filteredArchiveText = filter_text(archiveText)
+
+        cosineSimilarity = textdistance.cosine.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+        jaccardSimilarity = textdistance.jaccard.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+        #editDistanceSimilarity = textdistance.levenshtein.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+        sorensenDiceSimilarity = textdistance.sorensen_dice.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+
+        if(overallSimilarity < 0.80 or cosineSimilarity < 0.95):
+            hasContentChanged = True
+            lastSavedDataIndex = i
+            basePayload = archiveData[i]['payload']
+            basePayloadText = archiveText
+            basePayloadFilteredText = filteredArchiveText
+
+        data = [overallSimilarity, styleSimilarity, structuralSimilarity,                cosineSimilarity, jaccardSimilarity, sorensenDiceSimilarity, hasContentChanged]
+        dataset.append(data)
+
+    
+    
+    df = pd.DataFrame(dataset, columns = ['similarity', 'styleSimilarity', 'structureSimilarity',                                           'cosine', 'jaccard', 'sorensen', 'changed']) 
+    print("Dataframe created")
+    
+    X = df.iloc[:, 0:6].values
+    y = df.iloc[:, 6].values
+    
+    sc = StandardScaler()
+    X_train = sc.fit_transform(X)
+    
+    
+    overallSimilarity = similarity(basePayload, newRecord['payload'])
+    styleSimilarity = style_similarity(basePayload, newRecord['payload'])
+    structuralSimilarity = structural_similarity(basePayload, newRecord['payload'])
+
+    archiveText = text_from_html(newRecord['payload'])
+    filteredArchiveText = filter_text(archiveText)
+
+    cosineSimilarity = textdistance.cosine.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    jaccardSimilarity = textdistance.jaccard.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    #editDistanceSimilarity = textdistance.levenshtein.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    sorensenDiceSimilarity = textdistance.sorensen_dice.normalized_similarity(basePayloadFilteredText , filteredArchiveText)
+    
+    X_test = [overallSimilarity, styleSimilarity, structuralSimilarity,                cosineSimilarity, jaccardSimilarity, sorensenDiceSimilarity]
+    
+    
+    print("Starting SVM Classification")
+    
+    regressor = svm.SVC()
+    regressor.fit(X_train, y)
+    y_pred = regressor.predict([X_test])
+    
+    return y_pred
+
+    
+    
+    
+    
+    
+