# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
GREEN = .0001 #deaths / pop
ORANGE = .0005 #deaths / pop
RED = 1 #deaths / pop
# ------------------------------------------
pop_data = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_deaths_US.csv')
test_data = os.path.join(BASE_PATH, 'csse_covid_19_daily_reports_us',
'05-19-2020.csv')
area_data = os.path.join(BASE_PATH, 'areas.csv')
pop_data = data.load_csv_data(pop_data)
test_data = data.load_csv_data(test_data)
area_data = data.load_csv_data(area_data)
population = []
tests = []
death_proportion = []
deaths_labels = []
areas = [] #square miles
densities = [] #store population density here for each state
for state in np.unique(pop_data['Province_State']):
if state == 'Grand Princess' or state == 'Diamond Princess':
continue
sum_pop = 0
for id_curr_state in range(len(pop_data['Province_State'])):
if pop_data['Province_State'][id_curr_state] == state:
import numpy as np
from sklearn.neighbors import (KNeighborsClassifier, DistanceMetric)
import json
import scipy
import matplotlib.pyplot as plt
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
N_NEIGHBORS = 5
MIN_CASES = 1000
NORMALIZE = True
# ------------------------------------------
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
features = []
targets = []
'''
for val in np.unique(confirmed["Country/Region"]):
df = data.filter_by_attribute(
confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
features.append(cases)
targets.append(labels)
features = np.concatenate(features, axis=0)
targets = np.concatenate(targets, axis=0)
predictions = {}
for _dist in ['minkowski', 'manhattan']:
import numpy as np
from sklearn.neighbors import (KNeighborsClassifier, DistanceMetric)
import json
import matplotlib.pyplot as plt
import seaborn as sns
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
N_NEIGHBORS = 5
MIN_CASES = 1000
NORMALIZE = True
# ------------------------------------------
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
deaths = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_deaths_global.csv')
deaths = data.load_csv_data(deaths)
recoveries = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_recovered_global.csv')
recoveries = data.load_csv_data(recoveries)
features = []
targets = []
#print(len(confirmed["Country/Region"]))
#print(len(deaths["Country/Region"]))
#print(len(recoveries["Country/Region"]))
N = len(confirmed["Country/Region"])
##############################################################################################################
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
N_NEIGHBORS = 5
MIN_CASES = 1000
NORMALIZE = True
# ------------------------------------------
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_confirmed_US.csv')
deaths = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_deaths_US.csv')
confirmed = data.load_csv_data(confirmed)
deaths = data.load_csv_data(deaths)
state_dict = {}
features_deaths = []
targets_deaths = []
i = 0
for val in np.unique(deaths["Province_State"]):
state_dict.update({i: val})
df = data.filter_by_attribute(deaths, "Province_State", val)
cases, labels = data.get_cases_chronologically(df)
label = i
new_labels = np.ones(labels.shape[0]) * i
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
# ------------------------------------------
"""
I am interested in looking at how the rate of cases affects the death rate (deaths/cases)
I am expecting that higher jumps in the number of cases increases the death rate
I will use linear regression then a neural network to see if there is a relationship, and if so what it is.
"""
"""
Loading data
SECTION 1 BELOW
"""
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
confirmed = data.load_csv_data(confirmed)
dead = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_deaths_global.csv')
dead = data.load_csv_data(dead)
confirmed_features = []
confirmed_targets = []
dead_features = []
dead_targets = []
for val in np.unique(confirmed["Country/Region"]):
df = data.filter_by_attribute(confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
confirmed_features.append(cases)
confirmed_targets.append(labels)
import os
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
MIN_CASES = 1000
# ------------------------------------------
death = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_deaths_global.csv')
death = data.load_csv_data(death)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
for val in np.unique(death["Country/Region"]):
df = data.filter_by_attribute(death, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
MIN_CASES = 1000
# ------------------------------------------
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19-covid-Confirmed.csv')
confirmed = data.load_csv_data(
"COVID-19/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
for val in np.unique(confirmed["Country/Region"]):
df = data.filter_by_attribute(confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
import os
import sklearn
import numpy as np
import json
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# ------------ HYPERPARAMETERS -------------
BASE_PATH = '../COVID-19/csse_covid_19_data/'
MIN_CASES = 1000
# ------------------------------------------
recovered = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_recovered_global.csv')
recovered = data.load_csv_data(recovered)
features = []
targets = []
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
cm = plt.get_cmap('jet')
NUM_COLORS = 0
LINE_STYLES = ['solid', 'dashed', 'dotted']
NUM_STYLES = len(LINE_STYLES)
for val in np.unique(recovered["Country/Region"]):
df = data.filter_by_attribute(recovered, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
cases = cases.sum(axis=0)
def load_global():
BASE_PATH = './COVID-19/csse_covid_19_data/'
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_confirmed_global.csv')
return data.load_csv_data(confirmed)
