Kaggle competition: COVID19 Global Forecasting (Week 2)
<SOS> metric: MCRMSLE Kaggle data source: covid19-global-forecasting-(week-2)<predict_on_test>
%matplotlib inline
InteractiveShell.ast_node_interactivity = "all"
pd.set_option('display.max_columns', 99)
 pd.set_option('display.max_rows', 99)
predictions = model_lgb.predict(test_set)
 predictions = pd.DataFrame({'idhogar': test_ids,
'Target': predictions})
 submission = submission_base.merge(predictions,
on = 'idhogar',
how = 'left' ).drop(labels = ['idhogar'], axis = 1)
 lgb_submission = submission['Target'] = submission['Target'].fillna(4 ).astype(np.int8)
 lgb_submission.to_csv('lgb_submission.csv', index = False )<train_model>
plt.rcParams['figure.figsize'] = [16, 10]
plt.rcParams['font.size'] = 14
sns.set_palette(sns.color_palette('tab20', 20))
model_lgb_balanced = model.fit(X_train_balanced, y_train_balanced, early_stopping_rounds = 20,
eval_metric = macro_f1_score,
eval_set = [(X_train_balanced, y_train_balanced),(X_test_balanced, y_test_balanced)],
eval_names = ['train_balanced', 'valid_balanced'],
verbose = 200 )<predict_on_test>
COMP = '.. /input/covid19-global-forecasting-week-2'
DATEFORMAT = '%Y-%m-%d'
def get_comp_data(COMP):
train = pd.read_csv(f'{COMP}/train.csv')
 test = pd.read_csv(f'{COMP}/test.csv')
 submission = pd.read_csv(f'{COMP}/submission.csv')
 print(train.shape, test.shape, submission.shape)
 train['Country_Region'] = train['Country_Region'].str.replace(',', '')
 test['Country_Region'] = test['Country_Region'].str.replace(',', '')
 train['Location'] = train['Country_Region'] + '-' + train['Province_State'].fillna('')
 test['Location'] = test['Country_Region'] + '-' + test['Province_State'].fillna('')
 train['LogConfirmed'] = to_log(train.ConfirmedCases)
 train['LogFatalities'] = to_log(train.Fatalities)
 train = train.drop(columns=['Province_State'])
 test = test.drop(columns=['Province_State'])
 country_codes = pd.read_csv('.. /input/covid19-metadata/country_codes.csv', keep_default_na=False)
 train = train.merge(country_codes, on='Country_Region', how='left')
 test = test.merge(country_codes, on='Country_Region', how='left')
 train['DateTime'] = pd.to_datetime(train['Date'])
 test['DateTime'] = pd.to_datetime(test['Date'])
 return train, test, submission
def process_each_location(df):
dfs = []
for loc, df in tqdm(df.groupby('Location')) :
df = df.sort_values(by='Date')
 df['Fatalities'] = df['Fatalities'].cummax()
df['ConfirmedCases'] = df['ConfirmedCases'].cummax()
df['LogFatalities'] = df['LogFatalities'].cummax()
df['LogConfirmed'] = df['LogConfirmed'].cummax()
df['LogConfirmedNextDay'] = df['LogConfirmed'].shift(-1)
 df['ConfirmedNextDay'] = df['ConfirmedCases'].shift(-1)
 df['DateNextDay'] = df['Date'].shift(-1)
 df['LogFatalitiesNextDay'] = df['LogFatalities'].shift(-1)
 df['FatalitiesNextDay'] = df['Fatalities'].shift(-1)
 df['LogConfirmedDelta'] = df['LogConfirmedNextDay'] - df['LogConfirmed']
df['ConfirmedDelta'] = df['ConfirmedNextDay'] - df['ConfirmedCases']
df['LogFatalitiesDelta'] = df['LogFatalitiesNextDay'] - df['LogFatalities']
df['FatalitiesDelta'] = df['FatalitiesNextDay'] - df['Fatalities']
dfs.append(df)
 return pd.concat(dfs)
 def add_days(d, k):
return dt.datetime.strptime(d, DATEFORMAT)+ dt.timedelta(days=k)
 def to_log(x):
return np.log(x + 1)
 def to_exp(x):
return np.exp(x)- 1
predictions = model_lgb_balanced.predict(test_set)
 predictions = pd.DataFrame({'idhogar': test_ids,
'Target': predictions})
 submission = submission_base.merge(predictions,
on = 'idhogar',
how = 'left' ).drop(labels = ['idhogar'], axis = 1)
 lgb_balanced_submission = submission['Target'] = submission['Target'].fillna(4 ).astype(np.int8)
 lgb_balanced_submission.to_csv('lgb_submission.csv', index = False )<filter>
train = train.sort_values(by='Date')
 countries_latest_state = train[train['Date'] == TRAIN_END].groupby([
'Country_Region', 'continent', 'geo_region', 'country_iso_code_3'] ).sum() [[
'ConfirmedCases', 'Fatalities']].reset_index()
countries_latest_state['Log10Confirmed'] = np.log10(countries_latest_state.ConfirmedCases + 1)
 countries_latest_state['Log10Fatalities'] = np.log10(countries_latest_state.Fatalities + 1)
 countries_latest_state = countries_latest_state.sort_values(by='Fatalities', ascending=False)
 countries_latest_state.to_csv('countries_latest_state.csv', index=False)
 countries_latest_state.shape
countries_latest_state.head()
test_ids = list(head.loc[head['Target'].isnull() , 'idhogar'] )<predict_on_test>
latest_loc = train[train['Date'] == TRAIN_END][['Location', 'ConfirmedCases', 'Fatalities']]
max_loc = train.groupby(['Location'])[['ConfirmedCases', 'Fatalities']].max().reset_index()
check = pd.merge(latest_loc, max_loc, on='Location')
 np.mean(check.ConfirmedCases_x == check.ConfirmedCases_y)
 np.mean(check.Fatalities_x == check.Fatalities_y)
 check[check.Fatalities_x != check.Fatalities_y]
check[check.ConfirmedCases_x != check.ConfirmedCases_y]
def submit(model, train, train_labels, test, test_ids):

 model.fit(train, train_labels)
 predictions = model.predict(test)
 predictions = pd.DataFrame({'idhogar': test_ids,
'Target': predictions})
 submission = submission_base.merge(predictions,
on = 'idhogar',
how = 'left' ).drop(labels = ['idhogar'], axis = 1)
 submission['Target'] = submission['Target'].fillna(4 ).astype(np.int8)
 return submission<save_to_csv>
regional_progress = train_clean.groupby(['DateTime', 'continent'] ).sum() [['ConfirmedCases', 'Fatalities']].reset_index()
regional_progress['Log10Confirmed'] = np.log10(regional_progress.ConfirmedCases + 1)
 regional_progress['Log10Fatalities'] = np.log10(regional_progress.Fatalities + 1)
 regional_progress = regional_progress[regional_progress.continent != '
rf_submission = submit(RandomForestClassifier(n_estimators = 100,
random_state=10, n_jobs = -1),
train_set, train_labels, test_set, test_ids)
 rf_submission.to_csv('rf_submission.csv', index = False )<save_to_csv>
countries_0301 = country_progress[country_progress.Date == '2020-03-01'][[
'Country_Region', 'ConfirmedCases', 'Fatalities']]
countries_0331 = country_progress[country_progress.Date == '2020-03-31'][[
'Country_Region', 'ConfirmedCases', 'Fatalities']]
countries_in_march = pd.merge(countries_0301, countries_0331, on='Country_Region', suffixes=['_0301', '_0331'])
 countries_in_march['IncreaseInMarch'] = countries_in_march.ConfirmedCases_0331 /(countries_in_march.ConfirmedCases_0301 + 1)
 countries_in_march = countries_in_march[countries_in_march.ConfirmedCases_0331 > 200].sort_values(
 by='IncreaseInMarch', ascending=False)
 countries_in_march.tail(15 )
rf_balanced_submission = submit(RandomForestClassifier(n_estimators = 100,
random_state=10, n_jobs = -1),
train_set_balanced, train_labels_balanced, test_set, test_ids)
 rf_balanced_submission.to_csv('rf_balanced_submission.csv', index = False )<load_from_csv>
train_clean['Geo
latest = train_clean[train_clean.Date == '2020-03-31'][[
'Geo
daily_confirmed_deltas = train_clean[train_clean.Date >= '2020-03-17'].pivot(
 'Geo
daily_confirmed_deltas = latest.merge(daily_confirmed_deltas, on='Geo
daily_confirmed_deltas.shape
daily_confirmed_deltas.head()
daily_confirmed_deltas.to_csv('daily_confirmed_deltas.csv', index=False )
data=pd.read_csv('.. /input/train.csv' )<filter>
deltas = train_clean[np.logical_and(
 train_clean.LogConfirmed > 2,
~train_clean.Location.str.startswith('China')
)].dropna().sort_values(by='LogConfirmedDelta', ascending=False)
 deltas['start'] = deltas['LogConfirmed'].round(0)
 confirmed_deltas = pd.concat([
deltas.groupby('start')[['LogConfirmedDelta']].mean() ,
deltas.groupby('start')[['LogConfirmedDelta']].std() ,
deltas.groupby('start')[['LogConfirmedDelta']].count()
], axis=1)
 deltas.mean()
confirmed_deltas.columns = ['avg', 'std', 'cnt']
confirmed_deltas
confirmed_deltas.to_csv('confirmed_deltas.csv' )
data[(data['dependency']=='no')&(data['SQBdependency']!=0)]<filter>
DECAY = 0.93
DECAY ** 7, DECAY ** 14, DECAY ** 21, DECAY ** 28
confirmed_deltas = train.groupby(['Location', 'Country_Region', 'continent'])[[
'Id']].count().reset_index()
GLOBAL_DELTA = 0.11
confirmed_deltas['DELTA'] = GLOBAL_DELTA
confirmed_deltas.loc[confirmed_deltas.continent=='Africa', 'DELTA'] = 0.14
confirmed_deltas.loc[confirmed_deltas.continent=='Oceania', 'DELTA'] = 0.06
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Korea South', 'DELTA'] = 0.011
confirmed_deltas.loc[confirmed_deltas.Country_Region=='US', 'DELTA'] = 0.15
confirmed_deltas.loc[confirmed_deltas.Country_Region=='China', 'DELTA'] = 0.01
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Japan', 'DELTA'] = 0.05
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Singapore', 'DELTA'] = 0.05
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Taiwan*', 'DELTA'] = 0.05
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Switzerland', 'DELTA'] = 0.05
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Norway', 'DELTA'] = 0.05
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Iceland', 'DELTA'] = 0.05
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Austria', 'DELTA'] = 0.06
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Italy', 'DELTA'] = 0.04
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Spain', 'DELTA'] = 0.08
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Portugal', 'DELTA'] = 0.12
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Israel', 'DELTA'] = 0.12
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Iran', 'DELTA'] = 0.08
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Germany', 'DELTA'] = 0.07
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Malaysia', 'DELTA'] = 0.06
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Russia', 'DELTA'] = 0.18
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Ukraine', 'DELTA'] = 0.18
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Brazil', 'DELTA'] = 0.12
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Turkey', 'DELTA'] = 0.18
confirmed_deltas.loc[confirmed_deltas.Country_Region=='Philippines', 'DELTA'] = 0.18
confirmed_deltas.loc[confirmed_deltas.Location=='France-', 'DELTA'] = 0.1
confirmed_deltas.loc[confirmed_deltas.Location=='United Kingdom-', 'DELTA'] = 0.12
confirmed_deltas.loc[confirmed_deltas.Location=='Diamond Princess-', 'DELTA'] = 0.00
confirmed_deltas.loc[confirmed_deltas.Location=='China-Hong Kong', 'DELTA'] = 0.08
confirmed_deltas.loc[confirmed_deltas.Location=='San Marino-', 'DELTA'] = 0.03
confirmed_deltas.shape, confirmed_deltas.DELTA.mean()
confirmed_deltas[confirmed_deltas.DELTA != GLOBAL_DELTA].shape, confirmed_deltas[confirmed_deltas.DELTA != GLOBAL_DELTA].DELTA.mean()
confirmed_deltas[confirmed_deltas.DELTA != GLOBAL_DELTA]
confirmed_deltas.describe()
data[(data['dependency']=='yes')&(data['SQBdependency']!=1)]<filter>
daily_log_confirmed = train_clean.pivot('Location', 'Date', 'LogConfirmed' ).reset_index()
daily_log_confirmed = daily_log_confirmed.sort_values(TRAIN_END, ascending=False)
 daily_log_confirmed.to_csv('daily_log_confirmed.csv', index=False)
 for i, d in tqdm(enumerate(pd.date_range(add_days(TRAIN_END, 1), add_days(TEST_END, 1)))) :
new_day = str(d ).split(' ')[0]
last_day = dt.datetime.strptime(new_day, DATEFORMAT)- dt.timedelta(days=1)
 last_day = last_day.strftime(DATEFORMAT)
 for loc in confirmed_deltas.Location.values:
confirmed_delta = confirmed_deltas.loc[confirmed_deltas.Location == loc, 'DELTA'].values[0]
daily_log_confirmed.loc[daily_log_confirmed.Location == loc, new_day] = daily_log_confirmed.loc[daily_log_confirmed.Location == loc, last_day] + \
confirmed_delta * DECAY ** i
data[(data['dependency']=='3')&(data['SQBdependency']!=9)]<feature_engineering>
train_clean['Geo
latest = train_clean[train_clean.Date == TRAIN_END][[
'Geo
daily_death_deltas = train_clean[train_clean.Date >= '2020-03-17'].pivot(
 'Geo
daily_death_deltas = latest.merge(daily_death_deltas, on='Geo
daily_death_deltas.shape
daily_death_deltas.head()
daily_death_deltas.to_csv('daily_death_deltas.csv', index=False )
data['dependency']=np.sqrt(data['SQBdependency'] )<filter>
death_deltas = train.groupby(['Location', 'Country_Region', 'continent'])[[
'Id']].count().reset_index()
GLOBAL_DELTA = 0.11
death_deltas['DELTA'] = GLOBAL_DELTA
death_deltas.loc[death_deltas.Country_Region=='China', 'DELTA'] = 0.005
death_deltas.loc[death_deltas.continent=='Oceania', 'DELTA'] = 0.08
death_deltas.loc[death_deltas.Country_Region=='Korea South', 'DELTA'] = 0.04
death_deltas.loc[death_deltas.Country_Region=='Japan', 'DELTA'] = 0.04
death_deltas.loc[death_deltas.Country_Region=='Singapore', 'DELTA'] = 0.05
death_deltas.loc[death_deltas.Country_Region=='Taiwan*', 'DELTA'] = 0.06
death_deltas.loc[death_deltas.Country_Region=='US', 'DELTA'] = 0.17
death_deltas.loc[death_deltas.Country_Region=='Switzerland', 'DELTA'] = 0.15
death_deltas.loc[death_deltas.Country_Region=='Norway', 'DELTA'] = 0.15
death_deltas.loc[death_deltas.Country_Region=='Iceland', 'DELTA'] = 0.01
death_deltas.loc[death_deltas.Country_Region=='Austria', 'DELTA'] = 0.14
death_deltas.loc[death_deltas.Country_Region=='Italy', 'DELTA'] = 0.07
death_deltas.loc[death_deltas.Country_Region=='Spain', 'DELTA'] = 0.1
death_deltas.loc[death_deltas.Country_Region=='Portugal', 'DELTA'] = 0.13
death_deltas.loc[death_deltas.Country_Region=='Israel', 'DELTA'] = 0.16
death_deltas.loc[death_deltas.Country_Region=='Iran', 'DELTA'] = 0.06
death_deltas.loc[death_deltas.Country_Region=='Germany', 'DELTA'] = 0.14
death_deltas.loc[death_deltas.Country_Region=='Malaysia', 'DELTA'] = 0.14
death_deltas.loc[death_deltas.Country_Region=='Russia', 'DELTA'] = 0.2
death_deltas.loc[death_deltas.Country_Region=='Ukraine', 'DELTA'] = 0.2
death_deltas.loc[death_deltas.Country_Region=='Brazil', 'DELTA'] = 0.2
death_deltas.loc[death_deltas.Country_Region=='Turkey', 'DELTA'] = 0.22
death_deltas.loc[death_deltas.Country_Region=='Philippines', 'DELTA'] = 0.12
death_deltas.loc[death_deltas.Location=='France-', 'DELTA'] = 0.14
death_deltas.loc[death_deltas.Location=='United Kingdom-', 'DELTA'] = 0.14
death_deltas.loc[death_deltas.Location=='Diamond Princess-', 'DELTA'] = 0.00
death_deltas.loc[death_deltas.Location=='China-Hong Kong', 'DELTA'] = 0.01
death_deltas.loc[death_deltas.Location=='San Marino-', 'DELTA'] = 0.05
death_deltas.shape
death_deltas.DELTA.mean()
death_deltas[death_deltas.DELTA != GLOBAL_DELTA].shape
death_deltas[death_deltas.DELTA != GLOBAL_DELTA].DELTA.mean()
death_deltas[death_deltas.DELTA != GLOBAL_DELTA]
death_deltas.describe()
data[['edjefe', 'edjefa', 'SQBedjefe']][data['edjefe']=='yes']<filter>
daily_log_deaths = train_clean.pivot('Location', 'Date', 'LogFatalities' ).reset_index()
daily_log_deaths = daily_log_deaths.sort_values(TRAIN_END, ascending=False)
 daily_log_deaths.to_csv('daily_log_deaths.csv', index=False)
 for i, d in tqdm(enumerate(pd.date_range(add_days(TRAIN_END, 1), add_days(TEST_END, 1)))) :
new_day = str(d ).split(' ')[0]
last_day = dt.datetime.strptime(new_day, DATEFORMAT)- dt.timedelta(days=1)
 last_day = last_day.strftime(DATEFORMAT)
 for loc in death_deltas.Location:
death_delta = death_deltas.loc[death_deltas.Location == loc, 'DELTA'].values[0]
daily_log_deaths.loc[daily_log_deaths.Location == loc, new_day] = daily_log_deaths.loc[daily_log_deaths.Location == loc, last_day] + \
death_delta * DECAY ** i
data[(data['edjefe']=='yes')&(data['edjefa']!='no')]<filter>
confirmed = []
fatalities = []
for id, d, loc in tqdm(test[['ForecastId', 'Date', 'Location']].values):
c = to_exp(daily_log_confirmed.loc[daily_log_confirmed.Location == loc, d].values[0])
 f = to_exp(daily_log_deaths.loc[daily_log_deaths.Location == loc, d].values[0])
 confirmed.append(c)
 fatalities.append(f )
data[(data['edjefa']=='yes')&(data['parentesco1']==1)][['edjefe', 'edjefa', 'parentesco1', 'escolari']]<filter>
my_submission = test.copy()
my_submission['ConfirmedCases'] = confirmed
my_submission['Fatalities'] = fatalities
my_submission.shape
my_submission.head()
data[data['edjefe']=='yes'][['edjefe', 'edjefa','age', 'escolari', 'parentesco1','male', 'female', 'idhogar']]<filter>
my_submission[[
'ForecastId', 'ConfirmedCases', 'Fatalities'
]].to_csv('submission.csv', index=False)
 print(DECAY)
 my_submission.head()
my_submission.tail()
my_submission.shape
<filter><EOS> 
end = dt.datetime.now()
print('Finished', end,(end - start ).seconds, 's' )