def build(self):
self.create_results_table()
states = utils.get_states()
election_years = utils.get_election_years(base_dir=BASE_DIR)
house_files = utils.get_election_files(BASE_DIR, election_years, 'house', states)
for hfile in house_files:
self.parse_results_data(hfile)
def build(self):
self.base_dir = BASE_DIR + 'divisions/'
self.create_electorates_table()
states = utils.get_states()
election_years = utils.get_election_years(base_dir=BASE_DIR)
election_files = utils.get_election_files(BASE_DIR, election_years, 'house', states)
for efile in election_files:
self.parse_electorate_file(efile)
def state():
if request.method == 'POST':
jobs = df[df.state == request.form['state']].groupby(
'soc_id').first().reset_index()
jobs = [job[1] for job in jobs.iterrows()]
return render_template('state_list.html',
jobs=jobs,
no_jobs=len(jobs),
state=request.form['state'])
else:
return render_template('state_search.html', states=utils.get_states())
def build(self):
self.create_parties_table()
states = utils.get_states()
election_years = utils.get_election_years(base_dir=BASE_DIR)
house_files = utils.get_election_files(BASE_DIR, election_years, "house", states)
senate_files = utils.get_election_files(BASE_DIR, election_years, "senate", states)
party_dictionary = {}
for fdata in house_files:
self.parse_party_data(fdata, party_dictionary)
for sdata in senate_files:
self.parse_party_data(sdata, party_dictionary)
self.insert_party_data(party_dictionary)
def _get_us_jobs():
with open('jobs_250.json', 'r') as f:
product = utils.tag_job_product(json.loads(''.join(f.readlines())))
df = pd.DataFrame(product,
columns=[
'soc_id', 'title', 'company', 'location',
'remote', 'relocation', 'tag'
])
tmp_columns = df.location.str.split(',', expand=True)
df['city'] = tmp_columns[0].str.strip()
df['state'] = tmp_columns[1].str.strip()
df = df.drop('location', axis=1)
return df[df.state.apply(lambda state: state in utils.get_states())]
def __init__(self):
self.states = get_states()
self.reports_file = 'reports.json'
self.reports_dir = 'reports/'
self.image_dir = 'static/images/'
self.api_endpoint = 'https://covid-api.com/api/'
self.dates_file = 'dates.json'
self.first_date = '2020-04-16'
self.dates = get_dates(self.first_date)
self.reports = []
self.reports_parsed = []
dump_json(self.dates_file, self.dates)
def __init__(self):
self.states = get_states()
self.dates_file = 'dates.json'
self.reports_file = 'reports.json'
self.reports = load_json(self.reports_file)
self.dates = load_json(self.dates_file)
self.danger = []
self.deaths = []
self.confirmed = []
self.fatality = []
self.deaths_growth = []
self.confirmed_growth = []
self.fatality_growth = []
self.scoreboard_items = 10
def get_geoid_list(params):
logging.info("enter get_geoid_list")
if params['queryGeoMode'] == 'states':
# process included states
states = params['queryLocation']
state_mode = params['queryLocationMode']
state_year = params['queryCensusYear']
if state_year not in census_years:
# only certain years are included
raise ValueError(
'Selected census year is {}, but only {} are supported'.format(
state_year, census_years))
if state_mode == 'include':
if len(states) > 0:
include_states = states
else:
# no query states are defined, return error
raise ValueError(
'Query mode is "include" but no state is included in query'
)
elif state_mode == 'exclude':
include_states = set(get_states()) - set(states)
if len(include_states) == 0:
raise ValueError(
'Query mode is "exclude" but all states are included in query'
)
else:
raise ValueError('Query state mode is incorrect')
# query census tracts
query = {}
query = query_mapping_states(params, include_states, query)
results = db_map['census_tracts_{}'.format(state_year)].find(query)
logging.info('query census tracts data: {}\n{}\n\n'.format(
str(query), str(results.count())))
GEOID_list = [r['GEOID'] for r in results]
elif params['queryGeoMode'] == 'geo':
GEOID_list = geo_find_census_tracts(params)
else:
raise ValueError('Query mode is incorrect')
return GEOID_list
obs = data_in["channel" + str(action)][time +
pretrain_length] # Observe
next_state = utils.state_gen(state_in, action, obs) # Go to next state
reward = obs # Reward
total_rewards += reward # Total Reward
exp_memory.add(
(state_in, action, reward, next_state)) # Add in exp memory
state_in = next_state
history_input = next_state
if (time > state_size
or episode != 0): # If sufficient minibatch is available
batch = exp_memory.sample(batch_size) # Sample without replacement
states = utils.get_states(
batch) # Get state,action,reward and next state from memory
actions = utils.get_actions(batch)
rewards = utils.get_rewards(batch)
next_state = utils.get_next_states(batch)
feed_dict = {q_network.input_in: next_state}
actuals_Q = sess.run(
q_network.out_layer,
feed_dict=feed_dict) # Get the Q values for next state
actuals = rewards + gamma * np.max(
actuals_Q, axis=1) # Make it actuals with discount factor
actuals = actuals.reshape(batch_size)
# Feed in here to get loss and optimise it
loss, _ = sess.run(
import datetime
import streamlit as st
import yaml
import utils
with open("params.yaml", "r") as f:
params = yaml.load(f, yaml.Loader)
st.title("Progressão Temporal COVID-19 Brasil")
data = utils.load_data(os.path.join(params['data_dir'], params['fname']))
rolling = st.sidebar.number_input("Rolling", min_value=1, step=1, max_value=14)
estados = st.sidebar.multiselect("Estados", options=utils.get_states(data))
per_millon = st.sidebar.checkbox("Por milhão")
starting_date = st.sidebar.date_input("A partir da data:",
min_value=utils.get_min_date(data),
value=utils.get_min_date(data))
cum_sum = st.sidebar.checkbox("Acumulado")
brazil = st.sidebar.checkbox("Nacional")
utils.show_graph_by_states(data,
estados=estados,
rolling=rolling,
per_millon=per_millon,
cum_sum=cum_sum,
brazil=brazil,
starting_date=starting_date)
sshmm.model = StateSplitingHMM.fit(
sshmm.model, data['train']['xs'],
args.max_iterations,
args.n_jobs,
)
sshmm.plot(
args,
image_path=os.path.join(image_dir, f'sshmm_{sshmm.num_states:02}'),
model=sshmm.model,
cluster2utt=sshmm.cluster2utt,
)
for iteration in range(args.num_split):
print(f'*'*20, f'iteration {iteration+1}', '*'*20, flush=True)
model_json = json.loads(sshmm.model.to_json())
state2emissionprob, _, _ = get_states(model_json)
max_entropy_state = max(state2emissionprob.items(), key=lambda x: entropy(list(x[1].values())))[0]
print(" Try temperal split")
t_model, t_logprob, t_new = sshmm.fit_split(data['train']['xs'], max_entropy_state, sshmm.temperal_split)
print()
print(" Try vertical split")
v_model, v_logprob, v_new = sshmm.fit_split(data['train']['xs'], max_entropy_state, sshmm.vertical_split)
print()
print(f" LogProb: temperal = {t_logprob:.3f}; vertical = {v_logprob:.3f}")
if t_logprob > v_logprob:
print(" Choose temperal split")
sshmm.model = t_model
