Kaggle competition: COVID19 Global Forecasting (Week 3)
RMSLE(df_val[(df_val['ConfirmedCases'].isnull() == False)]['ConfirmedCases'].values,df_val[(df_val['ConfirmedCases'].isnull() == False)]['ConfirmedCases_hat'].values )<compute_test_metric>
pd.set_option('mode.chained_assignment', None)
 test = pd.read_csv(".. /input/covid19-global-forecasting-week-3/test.csv")
 train = pd.read_csv(".. /input/covid19-global-forecasting-week-3/train.csv")
 train['Province_State'].fillna('', inplace=True)
 test['Province_State'].fillna('', inplace=True)
 train['Date'] = pd.to_datetime(train['Date'])
 test['Date'] = pd.to_datetime(test['Date'])
 train = train.sort_values(['Country_Region','Province_State','Date'])
 test = test.sort_values(['Country_Region','Province_State','Date'])
 train[['ConfirmedCases', 'Fatalities']] = train.groupby(['Country_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']].transform('cummax' )
RMSLE(df_val[(df_val['Fatalities'].isnull() == False)]['Fatalities'].values,df_val[(df_val['Fatalities'].isnull() == False)]['Fatalities_hat'].values )<feature_engineering>
def RMSLE(pred,actual):
return np.sqrt(np.mean(np.power(( np.log(pred+1)-np.log(actual+1)) ,2)))
 feature_day = [1,20,50,100,200,500,1000,5000,10000,15000,20000,50000,100000,200000]
def CreateInput(data):
feature = []
for day in feature_day:
data.loc[:,'Number day from ' + str(day)+ ' case'] = 0
if(train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].count() > 0):
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].max()
else:
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].min()
for i in range(0, len(data)) :
if(data['Date'].iloc[i] > fromday):
day_denta = data['Date'].iloc[i] - fromday
data['Number day from ' + str(day)+ ' case'].iloc[i] = day_denta.days
feature = feature + ['Number day from ' + str(day)+ ' case']
return data[feature]
feature_day = [1,20,50,100,200,500,1000]
def CreateInput(data):
feature = []
for day in feature_day:
data.loc[:,'Number day from ' + str(day)+ ' case'] = 0
if(train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].count() > 0):
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['ConfirmedCases'] < day)]['Date'].max()
else:
fromday = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].min()
for i in range(0, len(data)) :
if(data['Date'].iloc[i] > fromday):
day_denta = data['Date'].iloc[i] - fromday
data['Number day from ' + str(day)+ ' case'].iloc[i] = day_denta.days
feature = feature + ['Number day from ' + str(day)+ ' case']
return data[feature]
pred_data_all = pd.DataFrame()
for country in train['Country_Region'].unique() :
for province in train[(train['Country_Region'] == country)]['Province_State'].unique() :
df_train = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]
df_test = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
X_train = CreateInput(df_train)
 y_train_confirmed = df_train['ConfirmedCases'].ravel()
y_train_fatalities = df_train['Fatalities'].ravel()
X_pred = CreateInput(df_test)
 for day in sorted(feature_day,reverse = True):
feature_use = 'Number day from ' + str(day)+ ' case'
idx = X_train[X_train[feature_use] == 0].shape[0]
if(X_train[X_train[feature_use] > 0].shape[0] >= 50):
break
adjusted_X_train = X_train[idx:][feature_use].values.reshape(-1, 1)
 adjusted_y_train_confirmed = y_train_confirmed[idx:]
adjusted_y_train_fatalities = y_train_fatalities[idx:]
idx = X_pred[X_pred[feature_use] == 0].shape[0]
adjusted_X_pred = X_pred[idx:][feature_use].values.reshape(-1, 1)
 pred_data = test[(test['Country_Region'] == country)&(test['Province_State'] == province)]
max_train_date = train[(train['Country_Region'] == country)&(train['Province_State'] == province)]['Date'].max()
min_test_date = pred_data['Date'].min()
model = ExponentialSmoothing(adjusted_y_train_confirmed, trend = 'additive' ).fit()
y_hat_confirmed = model.forecast(pred_data[pred_data['Date'] > max_train_date].shape[0])
 y_train_confirmed = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['Date'] >= min_test_date)]['ConfirmedCases'].values
y_hat_confirmed = np.concatenate(( y_train_confirmed,y_hat_confirmed), axis = 0)
 model = ExponentialSmoothing(adjusted_y_train_fatalities, trend = 'additive' ).fit()
y_hat_fatalities = model.forecast(pred_data[pred_data['Date'] > max_train_date].shape[0])
 y_train_fatalities = train[(train['Country_Region'] == country)&(train['Province_State'] == province)&(train['Date'] >= min_test_date)]['Fatalities'].values
y_hat_fatalities = np.concatenate(( y_train_fatalities,y_hat_fatalities), axis = 0)
 pred_data['ConfirmedCases_hat'] = y_hat_confirmed
pred_data['Fatalities_hat'] = y_hat_fatalities
pred_data_all = pred_data_all.append(pred_data)
 df_val = pd.merge(pred_data_all,train[['Date','Country_Region','Province_State','ConfirmedCases','Fatalities']],on=['Date','Country_Region','Province_State'], how='left')
 df_val 
<save_to_csv>
!pip install pmdarima
submission = df_val[['ForecastId','ConfirmedCases_hat','Fatalities_hat']]
submission.columns = ['ForecastId','ConfirmedCases','Fatalities']
submission.to_csv('submission.csv', index=False )<compute_test_metric>
df_val = df_val_2
submission = df_val[['ForecastId','ConfirmedCases_hat','Fatalities_hat']]
submission.columns = ['ForecastId','ConfirmedCases','Fatalities']
submission = submission.round({'ConfirmedCases': 0, 'Fatalities': 0})
 submission.to_csv('submission.csv', index=False)
 submission