Kaggle competition: COVID19 Global Forecasting (Week 4)
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'b7ae4de7070725f8438f',
'b7b2e80ebbfbc0d677e2',
'b7b41f1b3460a7ac381c']<define_variables>
learn.fit_one_cycle(80, lr_max=5e-2 )
qids_18= ['b7b62ee04de1a2f61679',
'b7b9a6fbec4dfc0231b0',
'b7bb4fc55df868598b1b',
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'c24a2c7e306d75486eb5',
'c24bee609f7d36cfce91',
'c252e57a127394cdc670']<define_variables>
learn.fit_one_cycle(300, lr_max=1e-2 )
qids_19 = ['c2542370907e65b45b9d',
'c25ad0fce30ac8ce6e69',
'c263fbcf45d5d4c49be2',
'c2675895099730c8a28e',
'c26f59fbd29750ecfe89',
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'c2899537d66fdb0063a1',
'c28d2e0f1537102b5f4e',
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'cc16ce03cc79ea92ae2c',
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'cc25678c5159f362a3e7',
'cc29682d96f51544e1f7',
'cc2dff9b10dcd6fe012d',
'cc3ab69868757118d381',
'cc3dff47e3061dce5299',
'cc4069c442f819f2bf42',
'cc4107ba042c0e15b5aa',
'cc42a43fb4975e0cd280',
'cc4409dec3ac7d8acc22',
'cc4420029c793ae3048e',
'cc46c69d5c6257fdb77c',
'cc46e18f8dfd5fbc91f0',
'cc48d6e829d214acfa07',
'cc4c9970bbdf85f38310',
'cc517add38e5d89efa40',
'cc52a35b1d5e50c32f5a',
'cc53983b98375817f960',
'cc558beb8102b4de90af',
'cc5931a53d4fc2479e2d',
'cc59c7ebd65b5fae8ee6',
'cc649d65ecf9f6cee84d',
'cc65d6d145643b9443f4',
'cc65f6f4dc686d60a24c',
'cc6723e932290b4c92cb',
'cc6928391f7701b49c1b',
'cc6a741adfe008aec007',
'cc6b7d75f7ce8263cc10',
'cc6ea0c158ae15dcaf5e',
'cc6eb053a0637723a9bb',
'cc6edef2d54cd029405c',
'cc6f701a34856e5518d0',
'cc7127158ee2fd95da83',
'cc742f1d1c2e2d6850fc',
'cc7452042f83546a44d7',
'cc74990509b2744f39ca',
'cc7737056ae06e28e289',
'cc77a0b7a8f3b813dd46',
'cc785d946d53d9f73899',
'cc78bd39fbc23daaae64',
'cc7d0ec3f80d81c013bd',
'cc7ec38d3be2be9f52fb',
'cc846b2326717f8d1c19',
'cc85631e329c6a98d1f9',
'cc869d004512f6896506',
'cc8b19ad02ce1afbd180',
'cc8c29a5479ca7c8d7ef',
'cc8e109ea6da2140cfa4']<define_variables>
to_tst = to.new(test_df)
 to_tst.process()
to_tst.all_cols.head()
qids_20=['cc8fe31d6c57f66e1c23',
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'd6e07b9130bf2fa7257b']<define_variables>
tst_dl = dls.valid.new(to_tst)
 tst_dl.show_batch()
qids_21=['d6e394adbe473d21d39f',
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'e22240f2fb8a8bb06aaa']<define_variables>
learn.metrics = []
tst_preds,_ = learn.get_preds(dl=tst_dl )
qids_22=['e226b2fd842549fd06ea',
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'ed031f1c2bbfa1162d13',
'ed0a64f9f8bd6e2d1648',
'ed0e60d75f136f20baba']<define_variables>
res1 = np.expm1(tst_preds)
 res2 = list(map(lambda x: x[0], res1.numpy()))
res3 = list(map(lambda x: x[1], res1.numpy()))
submit = pd.DataFrame({'ConfirmedCases': res2, 'Fatalities': res3})
 submit.index = test_df.ForecastId
qids_23=['ed0f5064f3c67b2df91f',
'ed11ba2c41c26927f9d9',
'ed165d431802c6d74f3c',
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'f861b2142ad6998605c4',
'f862351578b51bb012a8']<define_variables>
submit = pd.concat([submit, outlier_df], sort=True ).sort_index()
qids_24=['f8694f97583bf9ab2412',
'f86c8298fda46d80e25c',
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'fffed08be2626f74b139']<define_variables>
test = submit.merge(test_ori[['ForecastId', 'Date', 'Country_Region', 'Province_State']], on='ForecastId', how='left')
 test.index = test_ori.ForecastId
submit.ConfirmedCases = test.groupby(['Country_Region', 'Province_State'] ).ConfirmedCases.cummax()
submit.Fatalities = test.groupby(['Country_Region', 'Province_State'] ).Fatalities.cummax()
qids_all = qids_1+qids_2+qids_3+qids_4+qids_5+qids_6+qids_7+qids_8+qids_9+qids_10+qids_11+qids_12+qids_13+qids_14+qids_15+qids_16+qids_17+qids_18+qids_19+qids_20+qids_21+qids_22+qids_23+qids_24<load_from_csv>
submit.to_csv('submission.csv')
 submit
qid = []
with open('/kaggle/input/quora-insincere-questions-classification/test.csv', 'r')as read_obj,open('result.csv', 'w', newline='')as write_obj:
reader = csv.reader(read_obj)
 writer = csv.writer(write_obj, lineterminator='
')
 all_lines = []
row = []
row.append('qid')
 row.append('prediction')
 all_lines.append(row)
 line = next(reader)
 for rr in reader:
new_row = []
new_row.append(rr[0])
 if rr[0] in qids_all:
new_row.append(1)
 else:
new_row.append(0)
 all_lines.append(new_row)
 writer.writerows(all_lines )<save_to_csv>
min_date = test_df.Date.min()
max_date = train_df.Date.max()
f, axes = plt.subplots(10, 1, figsize=(16, 60))
def plot_preds(country, ax):
subset = test_hlp[(test_hlp['Country_Region'] == country)]
idx = subset.index
dates = subset.Date
predicted = submit.iloc[idx].ConfirmedCases
predicted.index = dates
combined = pd.DataFrame({'pred': predicted})
 sns.lineplot(data=combined, ax=axes[ax] ).set_title(country)
 plot_preds('Italy', 0)
 plot_preds('Spain', 1)
 plot_preds('Germany', 2)
 plot_preds('Poland', 3)
 plot_preds('Czechia', 4)
 plot_preds('Russia', 5)
 plot_preds('Iran', 6)
 plot_preds('Sweden', 7)
 plot_preds('Japan', 8)
 plot_preds('Belgium', 9 )
<import_modules><EOS> 
submit.iloc[test_hlp[test_hlp['Country_Region'] == 'Poland'].index]