if val not in predictions:
predictions[val] = {}
predictions[val][_dist] = label.tolist()
with open('results/knn_diff.json', 'w') as f:
json.dump(predictions, f, indent=4)
'''
# ---- Find its 30 geographic neighbors ----
# ---- Then perform knn and predict ----
for val in np.unique(confirmed["Country/Region"]):
df = data.filter_by_attribute(confirmed, "Country/Region", val)
if df.shape[0] >= 2:
whole_data = df['5/19/20'].idxmax(axis=0)
df = df.loc[[whole_data]]
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 = {}
targets_geo_location = targets[:, 2:]
mykd = scipy.spatial.KDTree(targets_geo_location)
for _dist in ['minkowski', 'manhattan']:
for val in np.unique(confirmed["Country/Region"]):
# test data
df = data.filter_by_attribute(confirmed, "Country/Region", val)
cases, labels = data.get_cases_chronologically(df)
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import json
# ------------ HYPERPARAMETERS -------------
BASE_PATH = 'COVID-19/csse_covid_19_data/'
# ------------------------------------------
confirmed = os.path.join(BASE_PATH, 'csse_covid_19_time_series',
'time_series_covid19_confirmed_US.csv')
confirmed = data.load_csv_data(confirmed)
tmpFeatures = []
for val in np.unique(confirmed["Country_Region"]):
df = data.filter_by_attribute(confirmed, "Country_Region", val)
cases, _ = data.get_cases_chronologically(df)
tmpFeatures.append(cases)
tmpFeatures = np.concatenate(tmpFeatures, axis=0)
features = np.sum(tmpFeatures, axis=0)
newCases = np.zeros(features.shape, dtype=np.int32)
i = len(features) - 1
while i > 0:
newCases[i] = features[i] - features[i - 1]
i -= 1
newCases[0] = features[0]
dates = np.arange(len(newCases))
