Multiple commits to neo4j from R - r

I have collected some tweets using the twitteR package and thereafter exported them to a neo4j database using Nicole White's various tutorials. I extract the tweets to a dataframe called kdf and thereafter use functions from stringr for basic cleaning up as demonstrated by Nicole. I am then sending this to neo4j from R. The essential part of my code is:
library(RNeo4j)
graph = startGraph("http://localhost:7474/db/data/", username="xxxx", password="xxxx")
clear(graph)
addConstraint(graph, "Tweet", "id")
addConstraint(graph, "User", "username")
addConstraint(graph, "Hashtag", "hashtag")
addConstraint(graph, "Tags", "ent_tag")
query = "
CREATE (tweet:Tweet {id: {tweetID}})
SET tweet.text = {text}
CREATE (user:User {name: {Username}})
CREATE (user)-[:TWEETED]->(tweet)
FOREACH(reply_to_sn IN CASE {reply_to_sn} WHEN NULL then [] else [{reply_to_sn}] END |
MERGE (replytouser:User {username:{reply_to_sn}})
CREATE (tweet)-[:IN_REPLY_TO]->(replytouser)
)
FOREACH(retweet_sn IN CASE {retweet_sn} WHEN NULL THEN [] ELSE [{retweet_sn}] END |
MERGE(retweet_user:User {username: {retweet_sn}})
CREATE (tweet)-[:RETWEET_OF]->(retweet_user)
)
FOREACH(hastag_nodes IN CASE {hashtag_nodes} WHEN NULL then [] else [{hashtag_nodes}] END |
MERGE (h:Hashtag {hashtag :{hashtag_nodes}})
CREATE (tweet)-[:HASHTAG]->(h)
)
FOREACH(mentioned_users IN CASE {mentioned_users} WHEN NULL then [] else [{mentioned_users}] END |
MERGE (m:User {username :{mentioned_users}})
CREATE (tweet)-[:MENTIONED]->(m)
)
"
tx = newTransaction(graph)
for(i in 1:nrow(kdf)){
row = kdf[i, ]
appendCypher(tx, query,
tweetID=row$id,
text=row$text,
Username=row$screenName,
reply_to_sn=row$replyToSN,
retweet_sn=getRetweetSN(row$text),
hashtag_nodes=getHashtags(row$text),
mentioned_users=getMentions(row$text))
}
commit(tx)
What I have done thereafter is extracted named entities for all the text using Watson's Alchemy API. This is stored in a dataframe called ent_tbl. This contains three variables, tweetid, etext and etype. Now I am trying to export this data too to the same neo4j databse and join on the id of the tweets. This is the other part of the code:
query="
MATCH(t:ent_tag {id : $twid, type :$etype, text :$etext})
MATCH(tw:tweet {tweetID : $twid })
CREATE (tw)-[:HAS_ENT]->(t)
"
tx=newTransaction(graph)
for (i in 1:nrow(ent_tbl)){
row = ent_tbl[i,]
appendCypher(tx, query,
twid=row2$tweetid,
etype=row2$etype,
etext=row2$etext)
}
commit(tx)
While I do not get any errors on committing this, summary(graph) does not show me the relationship between the tags (t) and the tweets (tw) that I expected to see.
> summary(graph)
This To That
1 User TWEETED Tweet
2 Tweet RETWEET_OF User
3 Tweet HASHTAG Hashtag
4 Tweet MENTIONED User
5 Tweet IN_REPLY_TO User
Why would this happen?
This is my db.schema in neo4j:

That is because the MATCH does not find any tag or tweet so it breaks. If you want to add data to existing nodes, you should match them by ID and then set their properties. And you got to be consistent with labels and upper/lower cases. I think this is what you are looking for.
query="
MATCH(t:Tags {ent_tag : $twid})
MATCH(tw:Tweet {tweetID : $twid })
SET t.type=$etype, t.text=$etext
CREATE (tw)-[:HAS_ENT]->(t)
"
tx=newTransaction(graph)
for (i in 1:nrow(ent_tbl)){
row = ent_tbl[i,]
appendCypher(tx, query,
twid=row2$tweetid,
etype=row2$etype,
etext=row2$etext)
}
commit(tx)

Related

How to scrape options from dropdown list and store them in table?

I am trying to make an interactive dashboard with analysis, base on car side. I would like user to be able to pick car brand for example BMW, Audi etc. and base on this choise he will have only avaiablity to pick BMW/Audi etc. models. I have a problem after selecting each brand, I am not able to scrape the models that belongs to that brand. Page that I am scraping from:
main page --> https://www.otomoto.pl/osobowe/
sub car brand page example --> https://www.otomoto.pl/osobowe/audi/
I have tried to scrape every option, so later on I can maybe somehow clean the data to store only models
code:
otomoto_models - paste0("https://www.otomoto.pl/osobowe/"audi/")
models <- read_html(otomoto_models) %>%
html_nodes("option") %>%
html_text()
But it is just scraping the brands with other options avaiable on the page engine type etc. While after inspecting element I can clearly see models types.
otomoto <- "https://www.otomoto.pl/osobowe/"
brands <- read_html(otomoto) %>%
html_nodes("option") %>%
html_text()
brands <- data.frame(brands)
for (i in 1:nrow(brands)){
no_marka_pojazdu <- i
if(brands[i,1] == "Marka pojazdu"){
break
}
}
no_marka_pojazdu <- no_marka_pojazdu + 1
for (i in 1:nrow(brands)){
zuk <- i
if(substr(brands[i,1],1,3) == "Żuk"){
break
}
}
Modele_pojazdow <- as.character(brands[no_marka_pojazdu:zuk,1])
Modele_pojazdow <- removeNumbers(Modele_pojazdow)
Modele_pojazdow <- substr(Modele_pojazdow,1,nchar(Modele_pojazdow)-2)
Modele_pojazdow <- data.frame(Modele_pojazdow)
Above code is only to pick supported car brands on the webpage and store them in the data frame. With that I am able to create html link and direct everything to one selected brand.
I would like to have similar object to "Modele_pojazdow" but with models limited on previous selected car brand.
Dropdown list with models appears as white box with text "Model pojazdu" next to the "Audi" box on the right side.
Some may frown on the solution language being Python, but the aim of this is was to give some pointers (high level process). I haven't written R in a long time so Python was quicker.
EDIT: R script now added
General outline:
The first dropdown options can be grabbed from the value attribute of each node returned by using a css selector of #param571 option. This uses an id selector (#) to target the parent dropdown select element, and then option type selector in descendant combination, to specify the option tag elements within. The html to apply this selector combination to can be retrieved by an xhr request to the url you initially provided. You want a nodeList returned to iterate over; akin to applying selector with js document.querySelectorAll.
The page uses ajax POST requests to update the second dropdown based on your first dropdown choice. Your first dropdown choice determines the value of a parameter search[filter_enum_make], which is used in the POST request to the server. The subsequent response contains a list of the available options (it includes some case alternatives which can be trimmed out).
I captured the POST request by using fiddler. This showed me the request headers and params in the request body. Screenshot sample shown at end.
The simplest way to extract the options from the response text, IMO, is to regex the appropriate string out (I wouldn't normally recommend regex for working with html but in this case it serves us nicely). If you don't want to use regex, you can grab the relevant info from the data-facets attribute of the element with id body-container. For the non-regex version you need to handle unquoted nulls, and retrieve the inner dictionary whose key is filter_enum_model. I show a function re-write, at the end, to handle this.
The retrieved string is a string representation of a dictionary. This needs converting to an actual dictionary object which you can then extract the option values from. Edit: As R doesn't have a dictionary object a similar structure needs to be found. I will look at this when converting.
I create a user defined function, getOptions(), to return the options for each make. Each car make value comes from the list of possible items in the first dropdown. I loop those possible values, use the function to return a list of options for that make, and add those lists as values to a dictionary, results ,whose keys are the make of car. Again, for R an object with similar functionality to a python dictionary needs to be found.
That dictionary of lists needs converting to a dataframe which includes a transpose operation to make a tidy output of headers, which are the car makes, and columns underneath each header, which contain the associated models.
The whole thing can be written to csv at the end.
So, hopefully that gives you an idea of one way to achieve what you want. Perhaps someone else can use this to help write you a solution.
Python demonstration of this below:
import requests
from bs4 import BeautifulSoup as bs
import re
import ast
import pandas as pd
headers = {
'User-Agent' : 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36'
}
def getOptions(make): #function to return options based on make
data = {
'search[filter_enum_make]': make,
'search[dist]' : '5',
'search[category_id]' : '29'
}
r = requests.post('https://www.otomoto.pl/ajax/search/list/', data = data, headers = headers)
try:
# verify the regex here: https://regex101.com/r/emvqXs/1
data = re.search(r'"filter_enum_model":(.*),"new_used"', r.text ,flags=re.DOTALL).group(1) #regex to extract the string containing the models associated with the car make filter
aDict = ast.literal_eval(data) #convert string representation of dictionary to python dictionary
d = len({k.lower(): v for k, v in aDict.items()}.keys()) #find length of unique keys when accounting for case
dirtyList = list(aDict)[:d] #trim to unique values
cleanedList = [item for item in dirtyList if item != 'other' ] #remove 'other' as doesn't appear in dropdown
except:
cleanedList = [] # sometimes there are no associated values in 2nd dropdown
return cleanedList
r = requests.get('https://www.otomoto.pl/osobowe/')
soup = bs(r.content, 'lxml')
values = [item['value'] for item in soup.select('#param571 option') if item['value'] != '']
results = {}
# build a dictionary of lists to hold options for each make
for value in values:
results[value] = getOptions(value) #function call to return options based on make
# turn into a dataframe and transpose so each column header is the make and the options are listed below
df = pd.DataFrame.from_dict(results,orient='index').transpose()
#write to csv
df.to_csv(r'C:\Users\User\Desktop\Data.csv', sep=',', encoding='utf-8-sig',index = False )
Sample of csv output:
Example as sample json for alfa-romeo:
Example of regex match for alfa-romeo:
{"145":1,"146":1,"147":218,"155":1,"156":118,"159":559,"164":2,"166":39,"33":1,"Alfasud":2,"Brera":34,"Crosswagon":2,"GT":89,"GTV":7,"Giulia":251,"Giulietta":378,"Mito":224,"Spider":24,"Sportwagon":2,"Stelvio":242,"alfasud":2,"brera":34,"crosswagon":2,"giulia":251,"giulietta":378,"gt":89,"gtv":7,"mito":224,"spider":24,"sportwagon":2,"stelvio":242}
Example of the filter option list returned from function call with make parameter value alfa-romeo:
['145', '146', '147', '155', '156', '159', '164', '166', '33', 'Alfasud', 'Brera', 'Crosswagon', 'GT', 'GTV', 'Giulia', 'Giulietta', 'Mito', 'Spider', 'Sportwagon', 'Stelvio']
Sample of fiddler request:
Sample of ajax response html containing options:
<section id="body-container" class="om-offers-list"
data-facets='{"offer_seek":{"offer":2198},"private_business":{"business":1326,"private":872,"all":2198},"categories":{"29":2198,"161":953,"163":953},"categoriesParent":[],"filter_enum_model":{"145":1,"146":1,"147":219,"155":1,"156":116,"159":561,"164":2,"166":37,"33":1,"Alfasud":2,"Brera":34,"Crosswagon":2,"GT":88,"GTV":7,"Giulia":251,"Giulietta":380,"Mito":226,"Spider":25,"Sportwagon":2,"Stelvio":242,"alfasud":2,"brera":34,"crosswagon":2,"giulia":251,"giulietta":380,"gt":88,"gtv":7,"mito":226,"spider":25,"sportwagon":2,"stelvio":242},"new_used":{"new":371,"used":1827,"all":2198},"sellout":null}'
data-showfacets=""
data-pagetitle="Alfa Romeo samochody osobowe - otomoto.pl"
data-ajaxurl="https://www.otomoto.pl/osobowe/alfa-romeo/?search%5Bbrand_program_id%5D%5B0%5D=&search%5Bcountry%5D="
data-searchid=""
data-keys=''
data-vars=""
Alternative version of function without regex:
from bs4 import BeautifulSoup as bs
def getOptions(make): #function to return options based on make
data = {
'search[filter_enum_make]': make,
'search[dist]' : '5',
'search[category_id]' : '29'
}
r = requests.post('https://www.otomoto.pl/ajax/search/list/', data = data, headers = headers)
soup = bs(r.content, 'lxml')
data = soup.select_one('#body-container')['data-facets'].replace('null','"null"')
aDict = ast.literal_eval(data)['filter_enum_model'] #convert string representation of dictionary to python dictionary
d = len({k.lower(): v for k, v in aDict.items()}.keys()) #find length of unique keys when accounting for case
dirtyList = list(aDict)[:d] #trim to unique values
cleanedList = [item for item in dirtyList if item != 'other' ] #remove 'other' as doesn't appear in dropdown
return cleanedList
print(getOptions('alfa-romeo'))
R conversion and improved python:
Whilst converting to R I found a better way of extracting the parameters from a js file on the server. If you open dev tools you can see the file listed in the sources tab.
R (To be improved):
library(httr)
library(jsonlite)
url <- 'https://www.otomoto.pl/ajax/jsdata/params/'
r <- GET(url)
contents <- content(r, "text")
data <- strsplit(contents, "var searchConditions = ")[[1]][2]
data <- strsplit(as.character(data), ";var searchCondition")[[1]][1]
source <- fromJSON(data)$values$'573'$'571'
makes <- names(source)
for(make in makes){
print(make)
print(source[make][[1]]$value)
#break
}
Python:
import requests
import json
import pandas as pd
r = requests.get('https://www.otomoto.pl/ajax/jsdata/params/')
data = r.text.split('var searchConditions = ')[1]
data = data.split(';var searchCondition')[0]
items = json.loads(data)
source = items['values']['573']['571']
makes = [item for item in source]
results = {}
for make in makes:
df = pd.DataFrame(source[make]) ## build a dictionary of lists to hold options for each make
results[make] = list(df['value'])
dfFinal = pd.DataFrame.from_dict(results,orient='index').transpose() # turn into a dataframe and transpose so each column header is the make and the options are listed below
mask = dfFinal.applymap(lambda x: x is None) #tidy up None values to empty strings https://stackoverflow.com/a/31295814/6241235
cols = dfFinal.columns[(mask).any()]
for col in dfFinal[cols]:
dfFinal.loc[mask[col], col] = ''
print(dfFinal)

Query is unable to match parts after "/" or parts within "()" in the data

I have a search request written as
import sqlite3
conn = sqlite3.connect('locker_data.db')
c = conn.cursor()
def search1(teacher):
test = 'SELECT Name FROM locker_data WHERE Name or Email LIKE "%{0}%"'.format(teacher)
data1 = c.execute(test)
return data1
def display1(data1):
Display1 = []
for Name in data1:
temp1 = str(Name[0])
Display1.append("Name: {0}".format(temp1))
return Display1
def locker_searcher(teacher):
data = display1(search1(teacher))
return data
This allows me to search for the row containing "Mr FishyPower (Mr Swag)" or "Mr FishyPower / Mr Swag" with a search input of "FishyPower". However, when I try searching with an input of "Swag", I am then unable to find the same row.
In the search below, it should have given me the same search results.
The database is just a simple 1x1 sqlite3 database containing 'FishyPower / Mr Swag'
Search Error on 'Swag'
Edit: I technically did solve it by limiting the columns being searched to only 'Name' but I intended the code search both the 'Name' and 'Email' columns and output the results as long as the search in within either or both columns.
Edit2: SELECT Name FROM locker_data WHERE Email LIKE "%{0}%" or Name LIKE "%{0}%" was the right way to go.
I'm gonna guess that Mr. FishyPower's email address is something like mrFishyPower#something.com. The query is only comparing Email to teacher. If it was
WHERE Name LIKE "%{0}%"
OR Email LIKE "%{0}%"'
you would (probably) get the result you want.

Is there a way to fetch data from sqlite3 database into list using python more efficiently?

I have a list of words (100k+ elements). In database each word has corresponding id. I want to get these id-s into list as well.
I'm using following function, but it is very slow:
def fetch_id(word_list, cursor):
for word in word_list:
cursor.execute('SELECT id FROM entries_table WHERE word = ?',(word,))
data = cursor.fetchone()
if data is not None:
return data[0]
else: return None
Is there a way to make function faster?
cursor.execute("SELECT id FROM entries_table WHERE word in {}".format(str(tuple(word_list))))

[Scala/Scalding]: map ID to name

I am fairly new to Scalding and I am trying to write a scalding program that takes as input 2 datasets:
1) book_id_title: ('id,'title): contains the mapping between book ID and book title, Both are strings.
2) book_sim: ('id1, 'id2, 'sim): contains the similarity between pairs of books, identified by their IDs.
The goal of the scalding program is to replace each (id1, id2) in book_ratings with their respective titles by looking up the book_id_title table. However, I am not able to retrieve the title. I would appreciate it if someone could help with the getTitle() function below.
My scalding code is as follows:
// read in the mapping between book id and title from a csv file
val book_id_title =
Csv(book_file, fields=book_format)
.read
.project('id,'title)
// read in the similarity data from a csv file and map the ids to the titles
// by calling getTitle function
val result =
book_sim
.map(('id1, 'id2)->('title1, 'title2)) {
pair:(String,String)=> (getTitle(pair._1), getTitle(pair._2))
}
.write(out)
// function that searches for the id and retrieves the title
def getTitle(search_id: String) = {
val btitle =
book_id_title
.filter('id){id:String => id == search_id} // extract row matching the id
.project('title) // get the title
}
thanks
Hadoop is a batch processing system and there is no way to lookup data by index. Instead, you need to join book_id_title and book_sim by id, probably two times: for left and right ids. Something like:
book_sim.joinWithSmaller('id1->id, book_id_title).joinWithSmaller('id2->id, book_id_title)
I am not very familiar with the field-based API so consider the above as a pseudocode. You also need to add appropriate projections. Hopefully, it still gives you an idea.

What's wrong with my filter query to figure out if a key is a member of a list(db.key) property?

I'm having trouble retrieving a filtered list from google app engine datastore (using python for server side). My data entity is defined as the following
class Course_Table(db.Model):
course_name = db.StringProperty(required=True, indexed=True)
....
head_tags_1=db.ListProperty(db.Key)
So the head_tags_1 property is a list of keys (which are the keys to a different entity called Headings_1).
I'm in the Handler below to spin through my Course_Table entity to filter the courses that have a particular Headings_1 key as a member of the head_tags_1 property. However, it doesn't seem like it is retrieving anything when I know there is data there to fulfill the request since it never displays the logs below when I go back to iterate through the results of my query (below). Any ideas of what I'm doing wrong?
def get(self,level_num,h_key):
path = []
if level_num == "1":
q = Course_Table.all().filter("head_tags_1 =", h_key)
for each in q:
logging.info('going through courses with this heading name')
logging.info("course name filtered is %s ", each.course_name)
MANY MANY THANK YOUS
I assume h_key is key of headings_1, since head_tags_1 is a list, I believe what you need is IN operator. https://developers.google.com/appengine/docs/python/datastore/queries
Note: your indentation inside the for loop does not seem correct.
My bad apparently '=' for list is already check membership. Using = to check membership is working for me, can you make sure h_key is really a datastore key class?
Here is my example, the first get produces result, where the 2nd one is not
import webapp2 from google.appengine.ext import db
class Greeting(db.Model):
author = db.StringProperty()
x = db.ListProperty(db.Key)
class C(db.Model): name = db.StringProperty()
class MainPage(webapp2.RequestHandler):
def get(self):
ckey = db.Key.from_path('C', 'abc')
dkey = db.Key.from_path('C', 'def')
ekey = db.Key.from_path('C', 'ghi')
Greeting(author='xxx', x=[ckey, dkey]).put()
x = Greeting.all().filter('x =',ckey).get()
self.response.write(x and x.author or 'None')
x = Greeting.all().filter('x =',ekey).get()
self.response.write(x and x.author or 'None')
app = webapp2.WSGIApplication([('/', MainPage)],
debug=True)

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