Kaggle competition: COVID19 Global Forecasting (Week 4)
<SOS> metric: MCRMSLE Kaggle data source: covid19-global-forecasting-(week-4)<train_model>
!pip install fastai2
!pip install fast_tabnet
epochs = 5
if(has_cv):
history = model.fit(np.array(train_text), np.array(train_target), epochs = epochs, validation_data=(np.array(cv_text),np.array(cv_target)) , batch_size=1024)
 else:
history = model.fit(np.array(train_text), np.array(train_target), epochs = epochs, batch_size=1024 )<find_best_params>
PATH = '/kaggle/input/covid19-global-forecasting-week-4/'
train_df = pd.read_csv(PATH + 'train.csv', parse_dates=['Date'])
 test_df = pd.read_csv(PATH + 'test.csv', parse_dates=['Date'])
 add_datepart(train_df, 'Date', drop=False)
 add_datepart(test_df, 'Date', drop=False)
 train_df.shape
if(has_cv):
cv_predictions = model.predict(cv_text, batch_size=1024)
 thresholds = []
for thresh in np.arange(0.1, 0.501, 0.01):
thresh = np.round(thresh, 2)
 result = f1_score(cv_target,(cv_predictions>thresh ).astype(int))
thresholds.append([thresh, result])
 print("F1 score at threshold {0} is {1}".format(thresh, result))
thresholds.sort(key=lambda x: x[1], reverse=True)
 print("Best value {0}".format(thresholds[0]))
best_thresh = thresholds[0]
else:
best_thresh = 0.29<predict_on_test>
PATH1 = '/kaggle/input/covid19-country-data-wk3-release/'
meta_convert_fun = lambda x: np.float32(x)if x not in ['N.A.', '
meta_df = pd.read_csv(PATH1 + 'Data Join - RELEASE.csv', thousands=",",
converters={
' TFR ': meta_convert_fun,
'Personality_uai': meta_convert_fun,
} ).rename(columns=lambda x: x.strip())
 PATH2 = '/kaggle/input/countryinfo/'
countryinfo = pd.read_csv(PATH2 + 'covid19countryinfo.csv', thousands=",", parse_dates=['quarantine', 'schools', 'publicplace', 'gathering', 'nonessential'])
 testinfo = pd.read_csv(PATH2 + 'covid19tests.csv', thousands=",")
 countryinfo.rename(columns={'region': 'Province_State', 'country': 'Country_Region'}, inplace=True)
 testinfo.rename(columns={'region': 'Province_State', 'country': 'Country_Region'}, inplace=True)
 testinfo = testinfo.drop(['alpha3code', 'alpha2code', 'date'], axis=1)
 PATH3 = '/kaggle/input/covid19-forecasting-metadata/'
continent_meta = pd.read_csv(PATH3 + 'region_metadata.csv' ).rename(columns={'density': 'pop_density'})
 continent_meta = continent_meta[['Country_Region' ,'Province_State', 'continent', 'lat', 'lon', 'pop_density']]
recoveries_meta = pd.read_csv(PATH3 + 'region_date_metadata.csv', parse_dates=['Date'])
 def fill_unknown_state(df):
df.fillna({'Province_State': 'Unknown'}, inplace=True)
 for d in [train_df, test_df, meta_df, countryinfo, testinfo, continent_meta, recoveries_meta]:
fill_unknown_state(d )
if(has_cv):
predictions = model.predict(cv_text)
 predictions = np.around(predictions ).astype(int)
 df = pd.DataFrame({'pred': predictions.flatten() , 'actual': cv_target})
 df.head()
pd.crosstab(df['pred'], df['actual'], margins=True )<predict_on_test>
outliars = ['China']
out_inputs = {}
for out in outliars:
out_inputs[out] = train_df[train_df['Country_Region'] == out]
train_df.drop(train_df.index[train_df['Country_Region'] == out], inplace=True )
predictions = model.predict(test_input_padded )<save_to_csv>
test_ori = test_df.copy()
merge_cols = ['Province_State', 'Country_Region', 'Date']
test_hlp = test_df[merge_cols + ['ForecastId']]
fst_date = test_hlp.Date.min()
outlier_dfs = []
for out, in_df in out_inputs.items() :
last_date = in_df.Date.max()
merged = in_df[in_df['Date'] >= fst_date].merge(test_hlp, on=merge_cols, how='left')[['ForecastId', 'ConfirmedCases', 'Fatalities']]
future_test = test_hlp[(test_hlp['Country_Region'] == out)&(test_hlp['Date'] > last_date)]
to_add = in_df.groupby(['Province_State', 'Country_Region'] ).last().reset_index() [['Province_State', 'Country_Region', 'ConfirmedCases', 'Fatalities']]
merged_future = future_test.merge(to_add, on=['Province_State', 'Country_Region'], how='left')[['ForecastId', 'ConfirmedCases', 'Fatalities']]
merged = pd.concat([merged, merged_future], sort=True)
 test_df.drop(test_df[test_df['ForecastId'].isin(merged.ForecastId)].index, inplace=True)
 outlier_dfs.append(merged)
 outlier_df = pd.concat(outlier_dfs, sort=True )
predictions =(predictions>best_thresh ).astype(int)
 output = pd.DataFrame({'qid': test_data.qid, 'prediction': predictions.flatten() })
 output.to_csv('submission.csv', index=False )<load_from_csv>
outlier_df.index = outlier_df['ForecastId']
outlier_df.drop('ForecastId', axis=1, inplace=True )
')
<define_variables>
train_max_date = train_df.Date.max()
outlier_all = test_df[test_df['Date'] <= train_max_date].merge(train_df, on=merge_cols, how='left')[['ForecastId', 'ConfirmedCases', 'Fatalities']]
outlier_all.index = outlier_all.ForecastId
test_df.drop(test_df[test_df['ForecastId'].isin(outlier_all.ForecastId)].index, inplace=True)
 outlier_all.drop('ForecastId', axis=1, inplace=True )
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'09a7ac9095f12385190f',
'09a7bf2d425ffffecea9',
'09ac9dd2b3e868aa1069',
'09af37413f67b3b89269',
'09b03b95915efe016b6e',
'09b0b982fd58b8dbbdc8',
'09b1813115b500f0e001',
'09b2a338b44833090149',
'09b2dba2532dca659049',
'09b33542d29e13cfbc08',
'09b4ec131ae5a373c983',
'09b6ddc653cd0ad0bc9e',
'09b7d328dd3c8c2477d8',
'09b83f534f069eb5f487',
'09b966814204cb1c64ec',
'09bb294b0e70f3fc3d91',
'09beed69c1a8c3b747e3',
'09c05ae8fcaa884b2fc3',
'09c61a8b476510565fe8',
'09ca274d82362a585861',
'09cb53b1ea93bdf2e9d9',
'09cdbfeb48598fa08a7a',
'09cdff4771cb77524fee',
'09d29c2db1d9921fceb8',
'09d4776466467eee9fef',
'09db15a414297d0f268c',
'09db1c2d0792b8cc40f0',
'09db39cf02b961f6cbdf',
'09db8757e9f9d3b2e8dc',
'09dfb5e105160f9a637b',
'09dffce9097bc0c1daac',
'09e1b518929be3242450',
'09e27c7cc88dfdd17d02',
'09e4e26aa15b95c00cfe',
'09ecfb859ba0e3dcabf3',
'09ed83f55f634f7c2117',
'09effce3e4228d1b03b5',
'09f3b845aa50f8c5070c',
'09f63891648f5614d0fa',
'09f7d5a54e447a69622a',
'09f95425171f5dbafbdf',
'09fca25b50b3dda98c6d',
'09fcf0cf4fbc4c664dc0',
'09fd99cbcdc0fabd690e',
'09ff99cf14114f29b7cd',
'0a007d7bdb3049519ba5',
'0a04047e06579ca35835',
'0a06f42a7b81f61c890b',
'0a07d9d4c9f162821fa9',
'0a0997da4330a851a7a6',
'0a0a02f328ff3b4e38ca',
'0a0abc88ce385a732d9b',
'0a0b953ea1352695566f',
'0a0db95031f5f1121246',
'0a107d95eff33d014067',
'0a177ed56e347610f271',
'0a178450103b0a9b69e2',
'0a18274bb76aa4d89624',
'0a20a4a70eac6dd32c6a',
'0a213d7b9eb81e26e7e4',
'0a289b6054ca7148018e',
'0a28c45462932e4de216',
'0a28d39ea332d133fcac',
'0a29936ef726fa2b7231',
'0a2aab94ffa3e59f736b',
'0a2ac608be05e90d4c4a',
'0a35b5b8b6c9185b6f94',
'0a378b07890ed55a44af',
'0a3793720d89f457a09a',
'0a380c15852acde6b227',
'0a38b5616dcd797eb181',
'0a38c04ae38bed58673f',
'0a3b98fa2d66cf751cf1',
'0a3e43c0e2e208f56f29',
'0a3e7992422734f777ee',
'0a46e4524e04409c1dca',
'0a4767e7bc8bb7cb20cc',
'0a4ef1adf26bdbef106f',
'0a519d21ac1135f2be61']<define_variables>
outlier_df = pd.concat([outlier_df, outlier_all], sort=True)
 outlier_df
qids_2 = ['0a52b9e1b98589577a31',
'0a537c9f4263d4c46f3c',
'0a538cc84a6c8569a596',
'0a54a9b63fb83d700011',
'0a55af2345d7f54ef45f',
'0a5614570e92ed3be483',
'0a56d7fc8083688dccce',
'0a57e8e249315de4bb65',
'0a5e013b34c4d597dd13',
'0a5e2da8fe37f674102b',
'0a66a6820374f242bbd9',
'0a677c725e90f092f1c3',
'0a6ec421ffaca886e902',
'0a6f3694f413a41d7d32',
'0a753b76e94a89849eb3',
'0a75a264de2aa1b27d1d',
'0a764646f209d0b9d4b8',
'0a7d71475d20bea5f71a',
'0a7db19bbe1c2110152e',
'0a80d3a30d211710e849',
'0a82bcfe03666c8676b3',
'0a8556713053a7c7f11a',
'0a871882765c383dff25',
'0a87ce70bb697cefbf23',
'0a8a14081d38ad6f73fc',
'0a8b1cf28561ef50230d',
'0a8cd72b361da98c5e5c',
'0a8f177e1b914f834cd1',
'0a9445408237f500e544',
'0a97dcbaa4c5e28bcc55',
'0a981f352dcf12f75a18',
'0a9b30d01fba51131978',
'0a9ef222dfeae972ff39',
'0aa038093133cda60de9',
'0aa8f84e49c8caa4816f',
'0aa914884d099d863bab',
'0aaeb57e1d73c2f28571',
'0aaffca715894a8bde0f',
'0ab116e7680f50a901dd',
'0ab43f91885d82ea84fa',
'0ab567f648bbca6ad50c',
'0ab6ef36289cb76e6dc7',
'0ab754436a30b3c0d0ab',
'0abc101ab439d67fa73e',
'0ac8933d49812b1a8ca6',
'0acf5588770a8c5b7fe6',
'0ad3f4662fc3c246d951',
'0ad5db801c8c8e3c849d',
'0ad6d293e34a1ecc4c87',
'0ad868a23a6191fca236',
'0ad9039bfa403120678e',
'0adaceb3e2ca9e02e24c',
'0add8fc96efc316e4968',
'0adec67798f91749ae54',
'0ae1900b3ae6f723d9d9',
'0ae5d784fad248540e29',
'0aeb2db692a8b011f0ae',
'0aef9e07c5c6c8c5f93c',
'0af11afd22de348d61a4',
'0af14379690ae43bd859',
'0af199e4768fd1d05c2a',
'0af1c0f345bfd2ff9ba6',
'0af77f1cb5b4f00581e6',
'0b01339c7aa1ad984694',
'0b0195ee148c3e82c86e',
'0b04e5e538ae0fa00146',
'0b04f3b232523e314df9',
'0b066c571f2a5805aea3',
'0b07b5432fc614ca6cae',
'0b0899cca543b7622e93',
'0b1016059fa2521d3166',
'0b10273be5a189e170b2',
'0b1187687e1a046c516e',
'0b129a5b414f8760f8d9',
'0b13d7a0d4b76a0a1fcf',
'0b198e54143ab7bf2e33',
'0b1a3c38e9e8869b051d',
'0b2118093756d246e6e2',
'0b24b49b93d47005eec8',
'0b24cbf957171e468ef6',
'0b25220f40fd3cd85b5d',
'0b26550881e2664957e0',
'0b27f43123f82b4c8caf',
'0b295f1f4d6a2be5a8a1',
'0b2c49d60fe7c0360e9a',
'0b2e43c1965fdfc696f1',
'0b2f4365173488fb7460',
'0b2f648ad363b2aedb13',
'0b30cb2f0806b1389e55',
'0b31a075bc5e21040f29',
'0b32f586bcf3d2e6495e',
'0b3f9571dd4c0230e4d0',
'0b3fed4fdacb70217316',
'0b449cf99cfd493d6a60',
'0b44bd18fe0c2c08ee17',
'0b44f8537f7513ab157c',
'0b4b2f321132f5c776f1',
'0b51fda16bdf70566294',
'0b53c8c12ddc717a02df',
'0b54349c27a66f20f3d9',
'0b567aa4ee8dff8f13e2',
'0b591b56bb276b60c1bc',
'0b5a91b2d6445c498cec',
'0b5abc8ec7efeebe9c8d',
'0b5b4ddeb1ce45ff2aeb',
'0b5e2d8b910ff654323c',
'0b5f14861cb27de27190',
'0b610f9b29b4b76307cb',
'0b66810a38dc1009f368',
'0b69ddffaccf35e40543',
'0b6e5b62ef5ed9cb5bca',
'0b6f017510b3bcd84178',
'0b721264f6810a32d741',
'0b751a45c01ea883ba61',
'0b76d8355a802020d383',
'0b7737997dbe7982e44e',
'0b782bc08ae860911760',
'0b7dd285d788d5518cd4',
'0b7f3bab716d33caac60',
'0b8020091e227862f26b',
'0b830933b31c9c804a2b',
'0b83af02da948b5349d7',
'0b857c612bb7bec1cd98',
'0b887abac89f54d547f6',
'0b903119136b0da02c07',
'0b95b263904c991dae9a',
'0b9af37c2d424b742fc8',
'0b9e755f8ad4124f8cca',
'0b9f6ac8e93b6fcf4151',
'0ba14f1ca923baa00cb2',
'0ba30fe8a921eaadb865',
'0ba7f0691142d1d072cc',
'0ba9790713ef5dab34ae',
'0ba9dbdea2119c405017',
'0bab6ece33c3a619191a',
'0bafbda82ada068a18f4',
'0bb8ed8cba2c7ab773cf',
'0bbaa9f196ac410af140',
'0bbab1cc7bf98f1f9b45',
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'0bcde9ddfe41e05b156f',
'0bd29819558c83209eca',
'0bd67552fe49a4371cc2',
'0be4184ad07713d6b1f7',
'0be64ab436593e0fcd63',
'0be9b07d5685e4d2a324',
'0be9baed1b1f2caa83e9',
'0bea04a7965d68314712',
'0bea58baee1b65eb9466',
'0beb68a0459105207761',
'0beb7e281bb99ffe2f35',
'0bf156d4ef455997379a',
'0bf506ad68a9f5e59126',
'0bf61c7c0ffc22163e82',
'0bfe5692cd796bbcbc2c',
'0bffbc97db2275e58f44',
'0c014f522ecd820253b5',
'0c0287897f72676886ac',
'0c02f2167d23a7ddfcfe',
'0c07d99d5b7cf25d4972',
'0c0fe1a61bc9c5a94f41',
'0c11efec57456f856654',
'0c136029ef9ce0dfd0ea',
'0c13a8f9c462c0a91f0d',
'0c13ac072c486dd30e9c',
'0c1502d20092a8fdfb51',
'0c179b59e83474608e31',
'0c187054dec29e8075c0',
'0c18a57c3d3eb46a16bf',
'0c1cba2630b46987e628',
'0c1cea0beaa455b25a19',
'0c21b0fdbda1b0df02eb',
'0c28c2f8c9f82215d619',
'0c293af59415bb54f3a9',
'0c2c4d0ff185960ffe01',
'0c2e4b95d48852cf92e2',
'0c3010c8c36ab325a8f0',
'0c31744a42ebf86b77b2',
'0c33adae8f49bbf664fa',
'0c3ab11e85f9452af9a2',
'0c3bb916acaa9173325f',
'0c42cee074f7d17380dc',
'0c452fc2e8eedeea0272',
'0c4dd4a87e246bfb8576',
'0c4fe6f4a12c990713da',
'0c502724e5b8a8decd89',
'0c5a84149a19b38fdc00',
'0c5b7e2010fc69b6e34f',
'0c5bf7512ae52d3cfa6b',
'0c5dd0138909a31aca14',
'0c5feda959a0fd2752f3',
'0c66c4ca1fd490c0fa70',
'0c6836210ab9a87d3b29',
'0c6848ffdbd5c61441c9',
'0c6c8115cbd02f4d1122',
'0c71993f360e375fc457',
'0c73e91e390890a1741b',
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'0ca13ec1fee110f3beb8',
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'0cb734c0f5b1b5799ef9',
'0cbb6bc6f63831e29685',
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'0cc154e157ce8c3c55a8',
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'0ccfca659aceb3855c37',
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'0cfdbd656ad75fcf8c54',
'0d020dd4c1c326fb9b9f',
'0d03483a9713a80b6d55',
'0d039eba6d7b7bc8f4db',
'0d04f7c24bf70bc6588e',
'0d067bd4110f17512427',
'0d0b7348a4ce58a78f17',
'0d0de8aef22e4406deb1',
'0d0f3999f9be269ab367',
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'0d110ae5578992620647',
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'0d1dcbd2deed993db560',
'0d288eaf3571e8e87860',
'0d2bd44acf3af336dda5',
'0d2c3a8e0d578dae85f0',
'0d2d444a741653387505',
'0d2f53324b5b978f8771',
'0d3152edf303efa0a604',
'0d36b056aff5b74cc6db',
'0d3c035aa4df2b657b50',
'0d3fab59d9856c0b0ed1',
'0d40653f0c6bba475363',
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'1516b5d16d256d22536d']<define_variables>
idx_group = ['Country_Region', 'Province_State']
def day_reached_cases(df, name, no_cases=1):

 gb = df[df['ConfirmedCases'] >= no_cases].groupby(idx_group)
 return gb.Dayofyear.first().reset_index().rename(columns={'Dayofyear': name})
 def area_fatality_rate(df):

 gb = df[df['Fatalities'] >= 22].groupby(idx_group)
 res_df =(gb.Fatalities.last() / gb.ConfirmedCases.last() ).reset_index()
return res_df.rename(columns={0 : 'FatalityRate'} )
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'1f8efa62f5648d651a97',
'1f954180910769327e6c',
'1f968394bbc94a4c3e63']<define_variables>
def joined_data(df):
res = df.copy()
fatality = area_fatality_rate(train_df)
 first_nonzero = day_reached_cases(train_df, 'FirstCaseDay', 1)
 first_fifty = day_reached_cases(train_df, 'First50CasesDay', 50)
 res = pd.merge(res, continent_meta, how='left')
 res = pd.merge(res, recoveries_meta, how='left')
 res = pd.merge(res, meta_df, how='left')
 res = pd.merge(res, countryinfo, how='left')
 res = pd.merge(res, testinfo, how='left', left_on=idx_group, right_on=idx_group)
 res = pd.merge(res, fatality, how='left')
 res = pd.merge(res, first_nonzero, how='left')
 res = pd.merge(res, first_fifty, how='left')
 return res
train_df = joined_data(train_df)
 test_df = joined_data(test_df )
qids_4 = ['1f97f1551ff5dc504fc6',
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train_df.FirstCaseDay.isna().sum()
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'353e7b871adfcda09198',
'35415637a4457da20d32',
'3543ab66a866853d0a8c',
'354a58710791ec1ed648']<define_variables>
def with_new_features(df):
res = df.copy()
add_datepart(res, 'quarantine', prefix='qua')
 add_datepart(res, 'schools', prefix='sql')
 res['DaysSinceFirst'] = res['Dayofyear'] - res['FirstCaseDay']
res['DaysSince50'] = res['Dayofyear'] - res['First50CasesDay']
res['DaysQua'] = res['Dayofyear'] - res['quaDayofyear']
res['DaysSql'] = res['Dayofyear'] - res['sqlDayofyear']
return res
train_df = with_new_features(train_df)
 test_df = with_new_features(test_df )
qids_6 = ['354e5064bb6911377195',
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'402bd4b2aac1541fce5e']<define_variables>
PATH4 = '/kaggle/input/covid19forecastforvalidset/'
valid_preds = pd.read_csv(PATH4 + 'forecast.csv')
 test_with_preds = test_df.merge(valid_preds, how='left')
 test_with_preds = test_with_preds[(test_with_preds['Date'] > train_df.Date.max())&(test_with_preds['Date'] <= '2020-04-28')]
test_with_preds.index += train_df.shape[0]
train_new = pd.concat([train_df, test_with_preds], sort=True ).sort_values(by='Date')
 train_new
qids_7 =['403384e69c5ca9896772',
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'4b7aa1ab2375458dc186']<define_variables>
train_new = train_new[train_new['Date'] >= '2020-02-20']
train_len = train_new[train_new['Date'] <= train_df['Date'].max() ].shape[0]
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'55b491365e83c09f29c0',
'55b5dfa99229f4b9d7ae',
'55b93332f1ec86d34621',
'55bb0ad505830f14208e',
'55be0356ab1942b4a695',
'55be2b8f6dc2dc08865a',
'55bee4e331b10e4bfb10',
'55c18dcc8b0847a7b859',
'55c39c4b33e6134e981a',
'55c6aba78e2ca7a05790',
'55c878de0423eb13138c',
'55c8bd64e149ae5025de',
'55c8dbcaec6687e73f52',
'55ca6b7b8d285acc2fac',
'55d469e635f9c478aa5d',
'55dc3c6b79e4e3534a0e',
'55e448d4bc4153ccfc0e',
'55e459f8ea76caf1c172',
'55e765f829995da0596f',
'55e92f53407790bf3bfa',
'55f01c3f73bb01635141',
'55f0c8c83d5a2507395f',
'55f0f7c344055766d2e2',
'55f20dcf71566a27a77b',
'55f5e770f4a8b56f1fac',
'55f89fe5694abd7d11c5',
'55fb446acf88e3d0bbf0',
'55fbe908561b979f25f9']<define_variables>
cat_vars = ['Country_Region', 'Province_State',
'continent',
]
cont_vars = ['DaysSinceFirst', 'DaysSince50', 'Dayofyear',
'DaysQua', 'DaysSql', 'Recoveries',
'recovered', 'active3', 'newcases3', 'critical3',
'TRUE POPULATION', 'TFR', 'Personality_uai', 'murder',
'testper1m', 'positiveper1m',
'casediv1m', 'deathdiv1m',
'FatalityRate',
'lat', 'lon', 'pop_density', 'urbanpop', 'medianage', 'hospibed','healthperpop',
'smokers', 'lung',
]
dep_var = ['ConfirmedCases', 'Fatalities']
df = train_new[cont_vars + cat_vars + dep_var +['Date']].copy().sort_values('Date' )