Kaggle competition: COVID19 Global Forecasting (Week 4)
m3 = RandomForestRegressor(n_estimators=60, min_samples_leaf=3, max_features=0.5, n_jobs=-1)
 m3.fit(X_train, y_train)
 print_score(m3 )<load_from_csv>
plotly.offline.init_notebook_mode()
%matplotlib inline
def RMSLE(pred,actual):
return np.sqrt(np.mean(np.power(( np.log(pred+1)-np.log(actual+1)) ,2)) )
test = pd.read_csv('.. /input/test_V2.csv' )<feature_engineering>
pd.set_option('mode.chained_assignment', None)
 test = pd.read_csv(".. /input/covid19-global-forecasting-week-4/test.csv")
 train = pd.read_csv(".. /input/covid19-global-forecasting-week-4/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'] )
test['headshot_rate'] = test['headshotKills'] / test['kills']
test['headshot_rate'] = test['headshot_rate'].fillna(0)
 test['totalDistance'] = test['rideDistance'] + test['walkDistance'] + test['swimDistance']
test['playersJoined'] = test.groupby('matchId')['matchId'].transform('count')
 test['killsNorm'] = test['kills']*(( 100-test['playersJoined'])/100 + 1)
 test['damageDealtNorm'] = test['damageDealt']*(( 100-test['playersJoined'])/100 + 1)
 test['maxPlaceNorm'] = test['maxPlace']*(( 100-train['playersJoined'])/100 + 1)
 test['matchDurationNorm'] = test['matchDuration']*(( 100-test['playersJoined'])/100 + 1)
 test['healsandboosts'] = test['heals'] + test['boosts']
test['killsWithoutMoving'] =(( test['kills'] > 0)&(test['totalDistance'] == 0))
test_pred = test[to_keep].copy()
test_pred.fillna(0, inplace=True)
 test_pred.head()<save_to_csv>
train[['ConfirmedCases', 'Fatalities']] = train.groupby(['Country_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']].transform('cummax')
predictions = np.clip(a = m3.predict(test_pred), a_min = 0.0, a_max = 1.0)
 pred_df = pd.DataFrame({'Id' : test['Id'], 'winPlacePerc' : predictions})
 pred_df.to_csv("submission.csv", index=False )<load_from_csv>
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()
with tqdm(total=len(train['Country_Region'].unique())) as pbar:
for country in train['Country_Region'].unique() :
for province in train[(train['Country_Region'] == country)]['Province_State'].unique() :
with warnings.catch_warnings() :
warnings.filterwarnings("ignore")
 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)
 feature_use = X_pred.columns[0]
for i in range(X_pred.shape[1] - 1,0,-1):
if(X_pred.iloc[0,i] > 0):
feature_use = X_pred.columns[i]
break
idx = X_train[X_train[feature_use] == 0].shape[0]
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:]
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)
 pbar.update(1)
 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.loc[df_val['Fatalities_hat'] < 0,'Fatalities_hat'] = 0
df_val.loc[df_val['ConfirmedCases_hat'] < 0,'ConfirmedCases_hat'] = 0
df_val_2 = df_val.copy()
df_train = pd.read_csv('.. /input/train_V2.csv', index_col='Id')
 df_train.shape<feature_engineering>
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