napsternxg

import time

def rate_limited_caller(max_rps=100, sleep_time=10):
  num_calls = 1
  def caller(func):
    def wrapper(*args, **kwargs):
      nonlocal num_calls
      output = func(*args, **kwargs)
      num_calls += 1
      if (num_calls % max_rps) == 0:

napsternxg

https://w.wiki/fTM https://w.wiki/eom

napsternxg

from pathlib import Path
import shutil
import requests

REQUIRED_COLS = ['human', 'image', 'sex_or_gender', 'ethnic_group', 'date_of_birth', 'occupation', 'loc_aid',]

def parse_row(row):
    data = {}
    for c in REQUIRED_COLS:
        value = row[c]["value"]

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# Takes around 30 minutes just to show df.head()

%%time
wikidata_dump_path="/path/to/latest-all.json.bz2"
df = sql.read.option("multiline", "true").json(wikidata_dump_path)
df.head()

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digraph G {
    subgraph cluster_1 {
        style=filled;
        color=pink;
        //node [style=filled,color=white];
        "x_1" -> "z_1";
        label="encoder1";
        
    }

napsternxg

import numpy as np

class Embedding(object):
  def __init__(self, unk_token=None):
    self.unk_token = unk_token
    self.word2id = {unk_token: 0}
    self.id2word = [unk_token]
    self.vectors = []
    
  def __len__(self):

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# Brown Clusters

# Algorithm 2 taken from (Slide 15): http://aritter.github.io/courses/5525_slides/brown.pdf

import numpy as np
from collections import defaultdict
from scipy.sparse import csr_matrix


from sklearn.datasets import fetch_20newsgroups

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SELECT ?subClass ?subClassLabel ?desc WHERE {
  ?subClass wdt:P279* wd:Q5. # Here we are getting all subClasses of Human and its subclasses
  OPTIONAL {
    ?subClass rdfs:label ?desc.
    FILTER((LANG(?desc)) = "en")
  }
  SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}

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API_KEY="" # Get from https://core.ac.uk/services/api/
CORE_REQUEST_URL=f"https://core.ac.uk:443/api-v2/articles/get?metadata=true&fulltext=true&citations=true&similar=false&duplicate=false&urls=false&faithfulMetadata=false&apiKey={API_KEY}"
def get_paper_data(core_ids, batch_size=10):
    core_ids = list(core_ids)
    for i in range(0, len(core_ids), batch_size):
        batch = core_ids[i:i+batch_size]
        resp = requests.post(CORE_REQUEST_URL, json=batch)
        batch_resp_json = resp.json()
        yield batch_resp_json
        print(f"Found {len(batch_resp_json)} responses. Sleeping for 2 seconds.")

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def print_schema(schema, level=0):
    for item in schema:
        elementType = None
        elementTypeName = ""
        if hasattr(item.dataType, "elementType"):
            elementType = item.dataType.elementType
            elementTypeName = item.dataType.elementType.typeName()
        #print("{}{}".format("\t"*level, item))
        print("{}{}\t{}\t{}\t{}".format(
                "\t"*level,