from utils import create_and_add_mapping, populate
import elasticsearch
from pprint import pprint
es = elasticsearch.Elasticsearch()
index_name = "my_index"
if es.indices.exists(index_name):
es.indices.delete(index_name)
create_and_add_mapping(es, index_name)
populate(es, index_name)
results = es.search(
index=index_name,
body={"size": 0,
"aggs": {
"pterms": {"terms": {"field": "name", "size": 10}}
}
})
pprint(results)
results = es.search(
index=index_name,
body={"size": 0,
"aggs": {
"date_histo": {"date_histogram": {"field": "date", "interval": "month"}}
}
})
pprint(results)
import elasticsearch
from pprint import pprint
es = elasticsearch.Elasticsearch()
index_name = "my_index"
type_name = "my_type"
if es.indices.exists(index_name):
es.indices.delete(index_name)
from utils import create_and_add_mapping, populate
create_and_add_mapping(es, index_name, type_name)
populate(es, index_name, type_name)
results = es.search(index_name, type_name, {"query": {"match_all": {}}})
pprint(results)
results = es.search(index_name, type_name, {
"query": {
"term": {"name": {"boost": 3.0, "value": "joe"}}}
})
pprint(results)
results = es.search(index_name, type_name, {"query": {
"bool": {
"filter": {
"bool": {
"should": [
{"term": {"position": 1}},
{"term": {"position": 2}}]}
# show emails
print(context['mails'])
# require email for login
email = input('ingrese email para iniciar: ')
# show initial view
print(f'bienvenido de nuevo {email}')
context['email'] = email
initialLoginView(context)
# main loop to ask for queries
stop = False
while not stop:
selection = instruction_menu()
if not selection:
continue
elif selection == 'finish':
stop = True
continue
else:
selection(context)
if __name__ == "__main__":
client = MongoClient()
db = client['test-database']
context = {'db': db}
pdb.set_trace()
# if no data, populate database
mails = populate()
context['mails'] = mails
# run application
ui(context)
import elasticsearch
from pprint import pprint
es = elasticsearch.Elasticsearch()
index_name = "my_index"
type_name = "my_type"
from utils import create_and_add_mapping, populate
create_and_add_mapping(es, index_name, type_name)
populate(es, index_name, type_name)
results = es.search(index_name, type_name, {"query": {"match_all": {}}})
pprint(results)
results = es.search(index_name, type_name, {
"query": {
"query": {
"term": {"name": {"boost": 3.0, "value": "joe"}}}
}})
pprint(results)
results = es.search(index_name, type_name, {"query": {
"filtered": {
"filter": {
"or": [
{"term": {"position": 1}},
{"term": {"position": 2}}]
},
"query": {"match_all": {}}}}})
pprint(results)
EPOCHS = 20
BATCH_SIZE = [1, 2, 4, 8, 16, 32]
LR = [0.1, 0.01, 0.001, 0.0001]
LOSS = rmse
PATIENCE = 3
MAX_QUEUE_SIZE = 32
SHUFFLE = True
directory = '../dataset/training/'
if __name__ == '__main__':
model_name = input("Enter a name for your model: ")
dataset = {angle: [] for angle in range(MINIMUM_ANGLE, MAXIMUM_ANGLE + 1)}
populate(dataset, directory, '.png')
training_dataset, validation_dataset = split(
dataset, training_size=TRAINING_SIZE, validation_size=VALIDATION_SIZE)
distribution_hist('../visualizations/distribution_pre-balance.png',
training_dataset)
balance(training_dataset,
downsample_threshold=DOWNSAMPLE_THRESHOLD,
upsample_threshold=UPSAMPLE_THRESHOLD)
distribution_hist('../visualizations/distribution_post-balance.png',
training_dataset)
training_data = unpack(training_dataset)
def remove(self, email):
if isinstance(email, list) == False:
email = [email,]
file_name = populate('unsubscribe', email, self.name)
raw = run(self.members_unsubscribe_cmd, file_name, self.name)