Download all keywords from catalog.data.gov

datagov_cluster.py

import matplotlib.pyplot as mp
import nlp.processing.storage as nps
import numpy as np
import json
from datagov_model_convert import gen_key

MODEL = nps.LocalStorage()
MODEL.restore('model.dict.pkl')
# print(len(MODEL.save))
MODEL_KEYS = nps.LocalStorage()
MODEL_KEYS.restore('model_keys.pkl')

def compare_two_words(a, b):
  if a in MODEL_KEYS.save:
    a_id = MODEL_KEYS.save[a]
  else:
    a_id = -1

  if b in MODEL_KEYS.save:
    b_id = MODEL_KEYS.save[b]
  else:
    b_id = -1

  if a_id == -1 or b_id == -1:
    return 0
  key = gen_key(a_id, b_id)
  if key in MODEL.save:
    return MODEL.save[key]
  return 0


def categorize(groups, unseen, similarity=0):
  visited = set()
  for word in unseen:
    assessment = {}
    for c, w in groups.items():
      temp = []
      for x in w:
        temp.append(compare_two_words(word, x))
      assessment[c] = temp
    for c,a in assessment.items():
      if max(a) > similarity:
        groups[c].add(word)
        visited.add(word)

  return groups, (unseen-visited)


f = open('tags.json')
a = json.load(f)
f.close()

groups = {}
keywords = set(a.keys())
relatedness = 1001
change_in_r = 0
times_with_no_change = 0
groups['0'] = set([keywords.pop()])
lower_keywords = set([])
while len(keywords) > 0:
  print(len(keywords), len(lower_keywords), len(groups), relatedness, change_in_r)
  old_len = len(keywords)
  groups, keywords = categorize(groups, keywords, similarity=relatedness)
  relatedness -= 10
  change_in_r += 10
  groups, keywords = categorize(groups, keywords, similarity=relatedness)
  new_len = len(keywords)

  if old_len == new_len:
    times_with_no_change += 1
    relatedness += 10

  if times_with_no_change > 1:
    new_category = keywords.pop()
    groups[len(groups)] = set([new_category])
    times_with_no_change = 0

  if change_in_r > 100 and len(keywords) > 0:
    new_category = keywords.pop()
    groups[len(groups)] = set([new_category])
    change_in_r = 0
    relatedness -= 10
    # groupc = groups.copy()
    # for k,v in groupc.items():
    #   if len(v) == 1:
    #     # lower_keywords.add(v.pop())
    #     keywords.add(v.pop())
    #     del groups[k]
  # if len(groups) > 10:
  #   keywords.update(lower_keywords)
  #   lower_keywords = set([])

  output = {i:list(j) for i,j in groups.items()}
  # with open('output.json', 'w') as o:
  #   o.write(json.dumps(output))

# print(len(groups))
x = [float(m) for m in groups.keys()]
y = [float(len(z)) for z in groups.values()]
print(sum(y))
mp.bar(x, y)
mp.show()

datagov_connections.py

import matplotlib.pyplot as mp
import nlp.processing.storage as nps
import numpy as np

MODEL = nps.LocalStorage()
MODEL.restore('model.dict.pkl')
# print(len(MODEL.save))
MODEL_KEYS = nps.LocalStorage()
MODEL_KEYS.restore('model_keys.pkl')

condensed_keys = set([])

def dec_key(key):
  i = key.split(',')
  return int(i[0]), int(i[1])

for k,v in MODEL.save.items():
  i,j = dec_key(k)
  condensed_keys.add(i)
  condensed_keys.add(j)

print(len(condensed_keys))


# From the 93691 unique keys that appear more than once,
# there are connections between 91145 keys
# Since this is still to large to plot an NxN colormap matrix,
# Just do the clustering on the memory-optimized data

datagov_model_convert.py

import matplotlib.pyplot as mp
import nlp.processing.storage as nps
import numpy as np


def gen_key(i,j):
  if i < j:
    return "%d,%d" % (i, j)
  else:
    return "%d,%d" % (j, i)

def update_dict(key, v):
  global MODEL_DICT
  if key in MODEL_DICT.save.keys():
    MODEL_DICT.save[key] += v
  else:
    MODEL_DICT.save[key] = v


if __name__ == '__main__':
  MODEL = nps.LocalStorage()
  MODEL.restore('model.pkl')
  MODEL_DICT = nps.LocalStorage()
  MODEL_DICT.save = {}

  nonzero = np.nonzero(MODEL.save)
  print(len(nonzero))
  for i in np.transpose(nonzero):
    update_dict(gen_key(i[0], i[1]), MODEL.save[i[0]][i[1]])

  MODEL_DICT.backup("model.dict.pkl")

datagov_related_words.py

import matplotlib.pyplot as mp
import nlp.processing.storage as nps
import numpy as np

MODEL = nps.LocalStorage()
MODEL.restore('model.dict.pkl')
# print(len(MODEL.save))
MODEL_KEYS = nps.LocalStorage()
MODEL_KEYS.restore('model_keys.pkl')

INVERTED_KEYS = {j:i for i,j in MODEL_KEYS.save.items()}

condensed_keys = set([])

def dec_key(key):
  i = key.split(',')
  return int(i[0]), int(i[1])

related_words = {}
for k,v in MODEL.save.items():
  i,j = dec_key(k)
  if i == MODEL_KEYS.save['water']:
    related_words[INVERTED_KEYS[j]] = v
  condensed_keys.add(i)
  condensed_keys.add(j)

print(len(condensed_keys))
print(related_words)
print(MODEL_KEYS.save['water'])

datagov_wordnet.py

import json
import numpy as np
import nlp.processing.storage as nps
import sys

global OUTPUT, OUTPUT_INDEX

INPUT = nps.LocalStorage()
OUTPUT = nps.LocalStorage()
OUTPUT_INDEX = nps.LocalStorage()
OUTPUT_INDEX.save = {}
a = open('more_than_2.json')
REFERENCE = json.load(a)
a.close()
INPUT.save = {}
current_index = 0

def lookup_index(key):
    global current_index, OUTPUT_INDEX
    if key in OUTPUT_INDEX.save.keys():
        return OUTPUT_INDEX.save[key]
    current_index += 1
    OUTPUT_INDEX.save[key] = current_index
    return current_index

try:
    INPUT.restore('datasets.pkl')
    # unique_keywords = 292043
    unique_keywords = 93691
except:
    INPUT.save = {
        1: ['a', 'b', 'c'],
        2: ['a', 'c'],
        3: ['c', 'd'],
        4: ['e']
    }
    unique_keywords = 5

OUTPUT.save = np.zeros((unique_keywords, unique_keywords), dtype=np.int16)


total = len(INPUT.save)
now = 0
for dataset, kw in INPUT.save.items():
    now += 1
    print(now/total)
    if len(kw) > 1:
        if kw[0] in REFERENCE.keys():
            a = lookup_index(kw[0])
            for k in kw[1:]:
                if k in REFERENCE.keys():
                    b = lookup_index(k)
                    OUTPUT.save[a][b] += 1
                    if OUTPUT.save[a][b] < 0:
                        print('Data type not large enough :(')
                        print(now/total, OUTPUT.save[a][b])
                        sys.exit(1)

OUTPUT.backup('model.pkl')
OUTPUT_INDEX.backup('model_keys.pkl')
# print(OUTPUT.save)
print(OUTPUT_INDEX.save)

inv.py

import nlp.processing.storage as nps
import json

# INPUT = nps.LocalStorage()
# INPUT.restore('datasets.pkl')

# print(len(INPUT.save))
# count = 0
# for k,v in INPUT.save.items():
#   print(k,v)
#   count += 1
#   if count > 2:
#     break

jso = {}
a = open('output_top_483.json')
jso = json.load(a)
a.close()

more_than_1 = 0
for k, v in jso.items():
  if len(v) > 1:
    more_than_1 += 1

print(more_than_1)

kw_catalog.py

import requests
import nlp.processing.storage as nps
import threading
from threading import Lock

fil = 'keywords.pkl'
STORAGE = nps.LocalStorage()
STORAGE.save = {}
current = 0
concurrent = 0
batch = 100
all_threads = []

def get_total_count():
    # Returns the total number of datasets on catalog.data.gov
    return requests.get('https://catalog.data.gov/api/action/package_search').json()['result']['count']

def get_next(rows, start):
    # curl -sL
    # 'https://catalog.data.gov/api/action/package_search?rows=10&start=9'
    # | jq '.result.results[].id'
    # | jq '.result.results[].tags[].display_name'
    r = requests.get('https://catalog.data.gov/api/action/package_search?rows=%s&start=%s' % (rows, start)).json()
    r_id = []
    r_tags = []
    return r['result']['results']

def parse_dataset(result):
    r_id = result['id']
    r_tags = [i['display_name'] for i in result['tags']]

    return r_id, r_tags

class Work(threading.Thread):
    def __init__(self, threadID, name):
        threading.Thread.__init__(self)
        self.name = name
        self.tid = threadID
        self.lock = Lock()

    def run(self):
        global concurrent
        print("Starting...", self.name)
        results_list = get_next(batch, self.tid*batch)
        self.lock.acquire()
        for a, i in enumerate(results_list):
            i_s, i_t = parse_dataset(i)
            if i_s not in STORAGE.save:
                STORAGE.save[i_s] = i_t
        if int(self.name) % batch == 0:
            STORAGE.backup(fil)
        concurrent -= 1
        self.lock.release()
        print("Completed...", self.name)


if __name__ == "__main__":
    total = get_total_count()
    try:
        STORAGE.restore(fil)
    except:
        pass

    while current*batch < total:
        if concurrent < 5:
            t = Work(current, str(current))
            all_threads.append(t)
            t.start()
            concurrent += 1
            current += 1

    [i.join() for i in all_threads]
    STORAGE.backup(fil)

kw_grouping.py

import nlp.processing.storage as nps

fil = 'test.pkl'
STORAGE = nps.LocalStorage()
STORAGE.save = {}

keywords = {}

if __name__ == "__main__":
    try:
        STORAGE.restore(fil)
    except:
        pass

    # This should be equal to the total number of datasets
    # print(len(STORAGE.save))

    for dataset, kw in STORAGE.save.items():
        for k in kw:
            if k in keywords:
                keywords[k] += 1
            else:
                keywords[k] = 1
    most_frequent = dict(sorted(keywords.items(), key=lambda item: item[1]))
    print(most_frequent)

plot_model.py

import matplotlib.pyplot as mp
import nlp.processing.storage as nps

MODEL = nps.LocalStorage()
MODEL.restore('model.pkl')

# mp.plot(MODEL.save)
# mp.show()
print(MODEL.save[0][0])
print(MODEL.save[10][5])
print(MODEL.save[5][10])

References:

• ckan/ckanapi#53
• https://www.tutorialspoint.com/python/python_multithreading.htm
• https://stackoverflow.com/a/21355442
• https://stackoverflow.com/a/613218

See my work fork for the complete working example:

• https://gist.github.com/nickumia-reisys/ba260d8b776ebb39026b8927ba19278d