Kaggle competition: COVID19 Global Forecasting (Week 4)
def get_structure_adj(train):
Ss = []
for i in tqdm(range(len(train))):
seq_length = train["seq_length"].iloc[i]
structure = train["structure"].iloc[i]
sequence = train["sequence"].iloc[i]
cue = []
a_structures = { 
("A", "U"): np.zeros([seq_length, seq_length]), 
("C", "G"): np.zeros([seq_length, seq_length]), 
("U", "G"): np.zeros([seq_length, seq_length]), 
("U", "A"): np.zeros([seq_length, seq_length]), 
("G", "C"): np.zeros([seq_length, seq_length]), 
("G", "U"): np.zeros([seq_length, seq_length]),
}
a_structure = np.zeros([seq_length, seq_length])
 for i in range(seq_length):
if structure[i] == "(":
cue.append(i)
 elif structure[i] == ")":
start = cue.pop()
a_structures[(sequence[start], sequence[i])][start, i] = 1
a_structures[(sequence[i], sequence[start])][i, start] = 1
a_strc = np.stack([a for a in a_structures.values() ], axis = 2)
 a_strc = np.sum(a_strc, axis = 2, keepdims = True)
 Ss.append(a_strc)
 Ss = np.array(Ss)
 print(Ss.shape)
 return Ss
Ss = get_structure_adj(train)
 Ss_pub = get_structure_adj(test_pub)
 Ss_pri = get_structure_adj(test_pri )<data_type_conversions>
path_week3 = '/kaggle/input/covid19-global-forecasting-week-4'
df_train = pd.read_csv(f'{path_week3}/train.csv',parse_dates=['Date'], engine='python')
 df_test = pd.read_csv(f'{path_week3}/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
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()
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()
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)
 df_out.ForecastId = df_out.ForecastId.astype('int')
 df_out.tail()
df_out.to_csv('submission.csv', index=False )