Kaggle competition: COVID19 Global Forecasting (Week 2)
def transfrom_all_data(transformer, train, test, feature_list):
data = pd.concat([train[feature_list], test[feature_list]], axis=0 ).reset_index(drop=True)
 n = train.shape[0]
data_trans = transformer.fit_transform(data)
 train_trans = data_trans[:n, :]
test_trans = data_trans[n:, :]
return train_trans, test_trans<normalization>
PATH_WEEK2='/kaggle/input/covid19-global-forecasting-week-2'
df_train = pd.read_csv(f'{PATH_WEEK2}/train.csv')
 df_test = pd.read_csv(f'{PATH_WEEK2}/test.csv')
 df_train.head()
df_test.head()
df_train.rename(columns={'Country_Region':'Country'}, inplace=True)
 df_test.rename(columns={'Country_Region':'Country'}, inplace=True)
 df_train.rename(columns={'Province_State':'State'}, inplace=True)
 df_test.rename(columns={'Province_State':'State'}, inplace=True)
 df_train['Date'] = pd.to_datetime(df_train['Date'], infer_datetime_format=True)
 df_test['Date'] = pd.to_datetime(df_test['Date'], infer_datetime_format=True)
 df_train.info()
df_test.info()
y1_Train = df_train.iloc[:, -2]
y1_Train.head()
y2_Train = df_train.iloc[:, -1]
y2_Train.head()
EMPTY_VAL = "EMPTY_VAL"
def fillState(state, country):
if state == EMPTY_VAL: return country
return state
def make_pca_features(n_comp, train, test, feature_list, name, normalize=False, scaler=None):
pca = PCA(n_comp)
 train_pca, test_pca = transfrom_all_data(pca, train, test, feature_list)
 if normalize and scaler is not None:
train_pca = scaler.fit_transform(train_pca)
 test_pca = scaler.transform(test_pca)
 for i in range(n_comp):
train['{0}_{1}'.format(name, i)] = train_pca[:, i]
test['{0}_{1}'.format(name, i)] = test_pca[:, i]
return train, test<categorify>
X_Train = df_train.copy()
X_Train['State'].fillna(EMPTY_VAL, inplace=True)
 X_Train['State'] = X_Train.loc[:, ['State', 'Country']].apply(lambda x : fillState(x['State'], x['Country']), axis=1)
 X_Train.loc[:, 'Date'] = X_Train.Date.dt.strftime("%m%d")
 X_Train["Date"] = X_Train["Date"].astype(int)
 X_Train.head()
X_Test = df_test.copy()
X_Test['State'].fillna(EMPTY_VAL, inplace=True)
 X_Test['State'] = X_Test.loc[:, ['State', 'Country']].apply(lambda x : fillState(x['State'], x['Country']), axis=1)
 X_Test.loc[:, 'Date'] = X_Test.Date.dt.strftime("%m%d")
 X_Test["Date"] = X_Test["Date"].astype(int)
 X_Test.head()
def preprocess(data):
data['cp_time'] = data['cp_time'].map({24:0, 48:1, 72:2})
 data['cp_dose'] = data['cp_dose'].map({'D1':0, 'D2':1})
 return data<concatenate>
le = preprocessing.LabelEncoder()
X_Train.Country = le.fit_transform(X_Train.Country)
 X_Train['State'] = le.fit_transform(X_Train['State'])
 X_Train.head()
X_Test.Country = le.fit_transform(X_Test.Country)
 X_Test['State'] = le.fit_transform(X_Test['State'])
 X_Test.head()
df_train.head()
df_train.loc[df_train.Country == 'Afghanistan', :]
df_test.tail()
train_features, test_features = make_pca_features(params['n_comp_g'], train_features, test_features, g_features, 'g_pca' )<concatenate>
filterwarnings('ignore')
 le = preprocessing.LabelEncoder()
countries = X_Train.Country.unique()
df_out = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})
 for country in countries:
states = X_Train.loc[X_Train.Country == country, :].State.unique()
for state in states:
X_Train_CS = X_Train.loc[(X_Train.Country == country)&(X_Train.State == state), ['State', 'Country', 'Date', 'ConfirmedCases', 'Fatalities']]
y1_Train_CS = X_Train_CS.loc[:, 'ConfirmedCases']
y2_Train_CS = X_Train_CS.loc[:, 'Fatalities']
X_Train_CS = X_Train_CS.loc[:, ['State', 'Country', 'Date']]
X_Train_CS.Country = le.fit_transform(X_Train_CS.Country)
 X_Train_CS['State'] = le.fit_transform(X_Train_CS['State'])
 X_Test_CS = X_Test.loc[(X_Test.Country == country)&(X_Test.State == state), ['State', 'Country', 'Date', 'ForecastId']]
X_Test_CS_Id = X_Test_CS.loc[:, 'ForecastId']
X_Test_CS = X_Test_CS.loc[:, ['State', 'Country', 'Date']]
X_Test_CS.Country = le.fit_transform(X_Test_CS.Country)
 X_Test_CS['State'] = le.fit_transform(X_Test_CS['State'])
 model1 = XGBRegressor(n_estimators=1000)
 model1.fit(X_Train_CS, y1_Train_CS)
 y1_pred = model1.predict(X_Test_CS)
 model2 = XGBRegressor(n_estimators=1000)
 model2.fit(X_Train_CS, y2_Train_CS)
 y2_pred = model2.predict(X_Test_CS)
 df = pd.DataFrame({'ForecastId': X_Test_CS_Id, 'ConfirmedCases': y1_pred, 'Fatalities': y2_pred})
 df_out = pd.concat([df_out, df], axis=0)
<categorify><EOS> 
df_out.ForecastId = df_out.ForecastId.astype('int')
 df_out.tail()
df_out.to_csv('submission.csv', index=False )